{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting future\n",
      "Installing collected packages: future\n",
      "Successfully installed future-0.17.1\n"
     ]
    }
   ],
   "source": [
    "!pip install future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.7698500479884e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  1.0908199508708189e-10\n",
      "dw error:  2.1752635504596857e-10\n",
      "db error:  7.736978834487815e-12\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  6.395535042049294e-11\n",
      "dw error:  8.162011105764925e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.22e-08\n",
      "W2 relative error: 3.17e-10\n",
      "b1 relative error: 6.19e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 1.00e+00\n",
      "W2 relative error: 1.00e+00\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 9.09e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**WHY W1/W2 relative error so big??? But it seems works for the later tasks, though.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2940) loss: 2.305456\n",
      "(Epoch 0 / 6) train acc: 0.132000; val_acc: 0.148000\n",
      "(Iteration 101 / 2940) loss: 1.741313\n",
      "(Iteration 201 / 2940) loss: 1.696644\n",
      "(Iteration 301 / 2940) loss: 1.666521\n",
      "(Iteration 401 / 2940) loss: 1.680714\n",
      "(Epoch 1 / 6) train acc: 0.465000; val_acc: 0.455000\n",
      "(Iteration 501 / 2940) loss: 1.413644\n",
      "(Iteration 601 / 2940) loss: 1.384271\n",
      "(Iteration 701 / 2940) loss: 1.583755\n",
      "(Iteration 801 / 2940) loss: 1.587828\n",
      "(Iteration 901 / 2940) loss: 1.321483\n",
      "(Epoch 2 / 6) train acc: 0.479000; val_acc: 0.491000\n",
      "(Iteration 1001 / 2940) loss: 1.250635\n",
      "(Iteration 1101 / 2940) loss: 1.472712\n",
      "(Iteration 1201 / 2940) loss: 1.205562\n",
      "(Iteration 1301 / 2940) loss: 1.151552\n",
      "(Iteration 1401 / 2940) loss: 1.377854\n",
      "(Epoch 3 / 6) train acc: 0.529000; val_acc: 0.496000\n",
      "(Iteration 1501 / 2940) loss: 1.432997\n",
      "(Iteration 1601 / 2940) loss: 1.324082\n",
      "(Iteration 1701 / 2940) loss: 1.330296\n",
      "(Iteration 1801 / 2940) loss: 1.294705\n",
      "(Iteration 1901 / 2940) loss: 1.275392\n",
      "(Epoch 4 / 6) train acc: 0.572000; val_acc: 0.493000\n",
      "(Iteration 2001 / 2940) loss: 1.172677\n",
      "(Iteration 2101 / 2940) loss: 1.158635\n",
      "(Iteration 2201 / 2940) loss: 1.260698\n",
      "(Iteration 2301 / 2940) loss: 1.402335\n",
      "(Iteration 2401 / 2940) loss: 1.366997\n",
      "(Epoch 5 / 6) train acc: 0.555000; val_acc: 0.485000\n",
      "(Iteration 2501 / 2940) loss: 1.309867\n",
      "(Iteration 2601 / 2940) loss: 1.129316\n",
      "(Iteration 2701 / 2940) loss: 1.144158\n",
      "(Iteration 2801 / 2940) loss: 1.211390\n",
      "(Iteration 2901 / 2940) loss: 0.938518\n",
      "(Epoch 6 / 6) train acc: 0.568000; val_acc: 0.510000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "model = TwoLayerNet(hidden_dim=200, reg=0)\n",
    "solver = Solver(model, data,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                lr_decay=0.95,\n",
    "                num_epochs=6, batch_size=100,\n",
    "                print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.3004790897684924\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 1.00e+00\n",
      "W2 relative error: 1.00e+00\n",
      "W3 relative error: 1.00e+00\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.57e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 3.703542\n",
      "(Epoch 0 / 20) train acc: 0.300000; val_acc: 0.087000\n",
      "(Epoch 1 / 20) train acc: 0.360000; val_acc: 0.131000\n",
      "(Epoch 2 / 20) train acc: 0.540000; val_acc: 0.151000\n",
      "(Epoch 3 / 20) train acc: 0.640000; val_acc: 0.131000\n",
      "(Epoch 4 / 20) train acc: 0.740000; val_acc: 0.144000\n",
      "(Epoch 5 / 20) train acc: 0.760000; val_acc: 0.146000\n",
      "(Iteration 11 / 40) loss: 0.592608\n",
      "(Epoch 6 / 20) train acc: 0.860000; val_acc: 0.144000\n",
      "(Epoch 7 / 20) train acc: 0.880000; val_acc: 0.152000\n",
      "(Epoch 8 / 20) train acc: 0.920000; val_acc: 0.151000\n",
      "(Epoch 9 / 20) train acc: 0.940000; val_acc: 0.157000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.153000\n",
      "(Iteration 21 / 40) loss: 0.140280\n",
      "(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.155000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.158000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.160000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.156000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.156000\n",
      "(Iteration 31 / 40) loss: 0.083028\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.155000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.153000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.158000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.155000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.156000\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 3e-2\n",
    "learning_rate = 1e-3\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 12.140554\n",
      "(Epoch 0 / 20) train acc: 0.180000; val_acc: 0.144000\n",
      "(Epoch 1 / 20) train acc: 0.200000; val_acc: 0.134000\n",
      "(Epoch 2 / 20) train acc: 0.320000; val_acc: 0.141000\n",
      "(Epoch 3 / 20) train acc: 0.460000; val_acc: 0.128000\n",
      "(Epoch 4 / 20) train acc: 0.560000; val_acc: 0.126000\n",
      "(Epoch 5 / 20) train acc: 0.740000; val_acc: 0.121000\n",
      "(Iteration 11 / 40) loss: 1.091039\n",
      "(Epoch 6 / 20) train acc: 0.760000; val_acc: 0.121000\n",
      "(Epoch 7 / 20) train acc: 0.820000; val_acc: 0.115000\n",
      "(Epoch 8 / 20) train acc: 0.840000; val_acc: 0.120000\n",
      "(Epoch 9 / 20) train acc: 0.860000; val_acc: 0.116000\n",
      "(Epoch 10 / 20) train acc: 0.920000; val_acc: 0.124000\n",
      "(Iteration 21 / 40) loss: 0.583780\n",
      "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.125000\n",
      "(Epoch 12 / 20) train acc: 0.960000; val_acc: 0.125000\n",
      "(Epoch 13 / 20) train acc: 0.960000; val_acc: 0.127000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.131000\n",
      "(Iteration 31 / 40) loss: 0.141127\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.127000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.126000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.125000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.125000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-3\n",
    "weight_scale = 6e-2\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "Under same learning rates, training 5-layer net requires larger weight scale to get the similar effect.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.737438\n",
      "(Epoch 0 / 5) train acc: 0.109000; val_acc: 0.132000\n",
      "(Iteration 11 / 200) loss: 2.162651\n",
      "(Iteration 21 / 200) loss: 2.090867\n",
      "(Iteration 31 / 200) loss: 2.018186\n",
      "(Epoch 1 / 5) train acc: 0.275000; val_acc: 0.250000\n",
      "(Iteration 41 / 200) loss: 1.981777\n",
      "(Iteration 51 / 200) loss: 2.068232\n",
      "(Iteration 61 / 200) loss: 1.867105\n",
      "(Iteration 71 / 200) loss: 1.890011\n",
      "(Epoch 2 / 5) train acc: 0.334000; val_acc: 0.284000\n",
      "(Iteration 81 / 200) loss: 1.820878\n",
      "(Iteration 91 / 200) loss: 1.828953\n",
      "(Iteration 101 / 200) loss: 1.775645\n",
      "(Iteration 111 / 200) loss: 1.742216\n",
      "(Epoch 3 / 5) train acc: 0.383000; val_acc: 0.309000\n",
      "(Iteration 121 / 200) loss: 1.841431\n",
      "(Iteration 131 / 200) loss: 1.585739\n",
      "(Iteration 141 / 200) loss: 1.729091\n",
      "(Iteration 151 / 200) loss: 1.622635\n",
      "(Epoch 4 / 5) train acc: 0.383000; val_acc: 0.340000\n",
      "(Iteration 161 / 200) loss: 1.666527\n",
      "(Iteration 171 / 200) loss: 1.688242\n",
      "(Iteration 181 / 200) loss: 1.690546\n",
      "(Iteration 191 / 200) loss: 1.645597\n",
      "(Epoch 5 / 5) train acc: 0.416000; val_acc: 0.341000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.593166\n",
      "(Epoch 0 / 5) train acc: 0.129000; val_acc: 0.113000\n",
      "(Iteration 11 / 200) loss: 2.111220\n",
      "(Iteration 21 / 200) loss: 2.039877\n",
      "(Iteration 31 / 200) loss: 2.018731\n",
      "(Epoch 1 / 5) train acc: 0.363000; val_acc: 0.313000\n",
      "(Iteration 41 / 200) loss: 1.806572\n",
      "(Iteration 51 / 200) loss: 1.742289\n",
      "(Iteration 61 / 200) loss: 1.671258\n",
      "(Iteration 71 / 200) loss: 1.696137\n",
      "(Epoch 2 / 5) train acc: 0.387000; val_acc: 0.325000\n",
      "(Iteration 81 / 200) loss: 1.701761\n",
      "(Iteration 91 / 200) loss: 1.555248\n",
      "(Iteration 101 / 200) loss: 1.466293\n",
      "(Iteration 111 / 200) loss: 1.607911\n",
      "(Epoch 3 / 5) train acc: 0.451000; val_acc: 0.359000\n",
      "(Iteration 121 / 200) loss: 1.509614\n",
      "(Iteration 131 / 200) loss: 1.538492\n",
      "(Iteration 141 / 200) loss: 1.397253\n",
      "(Iteration 151 / 200) loss: 1.739936\n",
      "(Epoch 4 / 5) train acc: 0.512000; val_acc: 0.344000\n",
      "(Iteration 161 / 200) loss: 1.322458\n",
      "(Iteration 171 / 200) loss: 1.364097\n",
      "(Iteration 181 / 200) loss: 1.428755\n",
      "(Iteration 191 / 200) loss: 1.473869\n",
      "(Epoch 5 / 5) train acc: 0.488000; val_acc: 0.352000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.1395691798535431e-07\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.645642\n",
      "(Epoch 0 / 5) train acc: 0.124000; val_acc: 0.106000\n",
      "(Iteration 11 / 200) loss: 2.088633\n",
      "(Iteration 21 / 200) loss: 1.803275\n",
      "(Iteration 31 / 200) loss: 1.841604\n",
      "(Epoch 1 / 5) train acc: 0.358000; val_acc: 0.334000\n",
      "(Iteration 41 / 200) loss: 1.819536\n",
      "(Iteration 51 / 200) loss: 1.625641\n",
      "(Iteration 61 / 200) loss: 1.509015\n",
      "(Iteration 71 / 200) loss: 1.455214\n",
      "(Epoch 2 / 5) train acc: 0.437000; val_acc: 0.354000\n",
      "(Iteration 81 / 200) loss: 1.689215\n",
      "(Iteration 91 / 200) loss: 1.517109\n",
      "(Iteration 101 / 200) loss: 1.472368\n",
      "(Iteration 111 / 200) loss: 1.469442\n",
      "(Epoch 3 / 5) train acc: 0.530000; val_acc: 0.374000\n",
      "(Iteration 121 / 200) loss: 1.409688\n",
      "(Iteration 131 / 200) loss: 1.378106\n",
      "(Iteration 141 / 200) loss: 1.490877\n",
      "(Iteration 151 / 200) loss: 1.212083\n",
      "(Epoch 4 / 5) train acc: 0.570000; val_acc: 0.371000\n",
      "(Iteration 161 / 200) loss: 1.390217\n",
      "(Iteration 171 / 200) loss: 1.386502\n",
      "(Iteration 181 / 200) loss: 1.053158\n",
      "(Iteration 191 / 200) loss: 1.429966\n",
      "(Epoch 5 / 5) train acc: 0.630000; val_acc: 0.376000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.565602\n",
      "(Epoch 0 / 5) train acc: 0.136000; val_acc: 0.117000\n",
      "(Iteration 11 / 200) loss: 2.049317\n",
      "(Iteration 21 / 200) loss: 1.884257\n",
      "(Iteration 31 / 200) loss: 1.969992\n",
      "(Epoch 1 / 5) train acc: 0.377000; val_acc: 0.304000\n",
      "(Iteration 41 / 200) loss: 1.665587\n",
      "(Iteration 51 / 200) loss: 1.724182\n",
      "(Iteration 61 / 200) loss: 1.679331\n",
      "(Iteration 71 / 200) loss: 1.661172\n",
      "(Epoch 2 / 5) train acc: 0.429000; val_acc: 0.327000\n",
      "(Iteration 81 / 200) loss: 1.619044\n",
      "(Iteration 91 / 200) loss: 1.628576\n",
      "(Iteration 101 / 200) loss: 1.546423\n",
      "(Iteration 111 / 200) loss: 1.501505\n",
      "(Epoch 3 / 5) train acc: 0.503000; val_acc: 0.355000\n",
      "(Iteration 121 / 200) loss: 1.676098\n",
      "(Iteration 131 / 200) loss: 1.453082\n",
      "(Iteration 141 / 200) loss: 1.479443\n",
      "(Iteration 151 / 200) loss: 1.510560\n",
      "(Epoch 4 / 5) train acc: 0.534000; val_acc: 0.370000\n",
      "(Iteration 161 / 200) loss: 1.275220\n",
      "(Iteration 171 / 200) loss: 1.463405\n",
      "(Iteration 181 / 200) loss: 1.509620\n",
      "(Iteration 191 / 200) loss: 1.332900\n",
      "(Epoch 5 / 5) train acc: 0.504000; val_acc: 0.366000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 2450) loss: 2.630346\n",
      "(Epoch 0 / 5) train acc: 0.111000; val_acc: 0.104000\n",
      "(Iteration 101 / 2450) loss: 1.690089\n",
      "(Iteration 201 / 2450) loss: 1.812651\n",
      "(Iteration 301 / 2450) loss: 1.622023\n",
      "(Iteration 401 / 2450) loss: 1.782854\n",
      "(Epoch 1 / 5) train acc: 0.440000; val_acc: 0.419000\n",
      "(Iteration 501 / 2450) loss: 1.671519\n",
      "(Iteration 601 / 2450) loss: 1.460948\n",
      "(Iteration 701 / 2450) loss: 1.566810\n",
      "(Iteration 801 / 2450) loss: 1.743192\n",
      "(Iteration 901 / 2450) loss: 1.415770\n",
      "(Epoch 2 / 5) train acc: 0.522000; val_acc: 0.465000\n",
      "(Iteration 1001 / 2450) loss: 1.443984\n",
      "(Iteration 1101 / 2450) loss: 1.293498\n",
      "(Iteration 1201 / 2450) loss: 1.395992\n",
      "(Iteration 1301 / 2450) loss: 1.574489\n",
      "(Iteration 1401 / 2450) loss: 1.472146\n",
      "(Epoch 3 / 5) train acc: 0.538000; val_acc: 0.482000\n",
      "(Iteration 1501 / 2450) loss: 1.275994\n",
      "(Iteration 1601 / 2450) loss: 1.386997\n",
      "(Iteration 1701 / 2450) loss: 1.489169\n",
      "(Iteration 1801 / 2450) loss: 1.338114\n",
      "(Iteration 1901 / 2450) loss: 1.362172\n",
      "(Epoch 4 / 5) train acc: 0.556000; val_acc: 0.511000\n",
      "(Iteration 2001 / 2450) loss: 1.237682\n",
      "(Iteration 2101 / 2450) loss: 1.311178\n",
      "(Iteration 2201 / 2450) loss: 1.224965\n",
      "(Iteration 2301 / 2450) loss: 1.162058\n",
      "(Iteration 2401 / 2450) loss: 1.351968\n",
      "(Epoch 5 / 5) train acc: 0.579000; val_acc: 0.525000\n"
     ]
    },
    {
     "data": {
      "image/png": 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iosWJAV1MpuAJABSA3YdmsHbPUWw5cLynBUlGR0rsgUdEREREtAixbUFMOkgaP3YO5UoVgkYwB9xYQ6erTPrvH1fcNgSjIyUGcEREREREiwxn6BIYHSnhxJ5tKBULsJVBSVNlkm0IiIiIiIjIBQO6FKKqSCatMsk2BERERERE5IIBXQpRVSSTVplkGwIiIiIiInLBgC4FW4EUIF2VSbYhICIiIiIiFwzoUvBXlwSAvAiA9FUm2YaAiIiIiIhcsMplSt2oLumvpOla5ZKIiIiIiBYfBnQZittqIAzbEBARERERURQGdAkFg7et61fj6dPlVnXKLHrRERERERERheEaugQemTyDXRPTbX3injh5nq0GiIiIiIiopzhDF9PkVBlPnDzf0VDc1mBctxowpWMCXCdHRERERETJMaCLafzYOWvwZpITwSOTZzrSMceenAEEqNVV67ZgimaWa/KIiIiIiOjmE5lyKSK3ichzIvKSiJwVkd8y3EdE5PdF5O9E5EUReZ/vb78uIn/b/N+vZ70DvRa3uXddKWM6Zm1etYI5zZ+iOTlVxsOHz7SldT58+Awmp8qptp+IiIiIiG4eLjN0cwB2K6VeEJG3ATgtIt9USr3ku8+HALyn+b+7AfxHAHeLyCoAjwLYjEZW4mkROaKUupzpXvRQcdjD5dlarMfEmdHTAeP4sXPWNXn+WTrO4hERERERLV6RM3RKqTeVUi80//tHAL4LIBgx/AqAP1MNJwEUReSdALYD+KZS6lIziPsmgA9mugc9NDlVxo/fmuvqa9zabFJumwn0385ZPCIiIiKixS1WlUsRWQtgBMDzgT+VALzh+/f3mrfZbjc992dF5JSInLp48WKczeqZ8WPnUJuPM98WT8HLY2z7OkxOlZETMd5HB3x6e1hZk4iIiIho8XIuiiIitwB4GsBDSqkfZr0hSqmvAPgKAGzevLl7UVMKcdfPaQJz2mWx4GH50iGUK1XkRVCt1bH3yFlcvT6Huup8hA74orYn6XYSEREREdFgcZqhExEPjWDuCaXUYcNdygBu8/37Xc3bbLcPJP/sWBz/w7tXoeDl224reHns/eidGNu+DgUv3wrgKtVaR7EUAMiLYP+ODW3r42zbk3Q7iYiIiIhosLhUuRQAfwzgu0qp37Pc7QiA/6lZ7fIeAFeUUm8COAbgXhFZKSIrAdzbvG0g+WfH4njh/BU8cFcJxYLXuq1aq2PfM2ex75mzHWmTJnWlOoqd6GDQLziLR0RERERENy+XlMstAD4D4IyITDdv+xyANQCglPpDAH8J4MMA/g7ALIB/0fzbJRH5XQDfaT7ui0qpS9ltfm+NjpSw75mzsatcVmt1fH3mTfzwrfbHxXmevGFNnQ7w0lS5ZJVMIiIiIqLBJcqwVmuhbd68WZ06dWqhN8NIV5Z0mVXL2msH7sv0+Uz7UvDyHamdRERERETUWyJyWim1Oep+sapcUmNWbP+ODSg116n5581y5sKUmSh1YV0cq2QSEREREQ025yqXdMPoSMk4g3XHnqNdeb1urYuLqpIZJx3TdF8gXTooERERERGFY0CXoVuLBZQzahmwcthDZbbW1UDItr23Fgsd6Zi6aTmAjm0x3XfsyRlA0KrYGfZ4IiIiIiJKhimXGRrbvg5eRnmXw0uG8OqB+3Biz7auBUBhVTLjpGOa7lubVx3tF5jOSURERESULc7QpRRMNczlBJhPX2imF83Bw6pk7pqYNj7GtF1xtpVNz4mIiIiIssOALgVTqmFWwpqDR61ti7P2zbYeMCwd0/W+Jmx6TkRERESUHQZ0KZhSDbPg5aWtCIo/QFtR8HD1+px1bVqctW9hxravM7Y0MBVnMd3Xy0nbGrqwxxMRERERUTIM6FKImz6YF8E9P7MSJ16J6K3ejIEmp8rYe+QsKtUbDcj9/63ptWmjIyXr2re9R87GCujiNC233df18TcTNmonIiIiol5iY/EUthw4HjvN0ssJag5r7IoFD9fm5p1nAAXAqwfuwx17jsL27I/t3MTgoovYqJ2IiIiIssLG4j1gqhIZxSWYAxozcXHSOfXatLA1ag9NTGPLgeOYnCo7Py+5Y6N2IiIiIuo1plymoGdddh2axkJOdOq1aZNTZVy9Nhd630HpBzeIqYtRjdqJiIiIiLLGgC6l0ZFSxzo3TQRdC/R0d4SSb71aMN3Pxr/mrlfiBGimwi67JqZx6vVL2Hz7qr4N9OJUBiUiIiIiygIDugxcMQRzQPeCOaARzHk5aQU0Ww4cj5WiaZs16sbMWNzKm6bURQXg8ZPnMfGdN6wVPhdanMqgRERERERZ4Bq6DMSZgfmpty2J9dwrhz0IGkVPgmrzCnuPnAUQP63Pv82TU2VsOXAca/ccxa6JaZQrVSjcCJjSrrmLu7YsbF/8bRCinqfXRkdK2L9jA0rFAgSN2VMWRCEiIiKibuIMXQa2rl+NJ06et1aX9PvHH12P9dw/vjaHgzs34aGJaePfdapnWHPvfE5Q9xVjkeY2m9oiBPchKj3TZUYv7tqyOI3Kw55nIdgatRMRERERdQNn6FKanCrj6dNlp2AuiVpdOc1AjW1fZ5zFA9AWzAGNoG3i229g7KkZ49q/oLD0zIcPn2mb0ds1MY1HJs+03c82g2m7PWxf4jwPEREREdHNjgFdSqZ0wqxdqFSxctgz/k3fPjpSihVU1uZVR/qijS1gsq11e+Lk+bY0TVN7h7C1ZaMjJXz6njUdQZ2XE3j59lu5Ro2IiIiIFjMGdCn1It3v1mIBj95/Z0cw4+UFj95/Z+vfpS7MVIUFTLZ9V0DbrGKStWVfGt2Agzs3tT1m/OMbMf7gRq5RIyIiIiJq4hq6lOKu94rLy9+oZHnq9Uv46vNvoK4U8iLY+Qu3tQUzY9vXWdfaxSFoBGWliCqXYfseDPaSrC2zPYYBHBERERFRA2foUhrbvg5eLs6Kr3DD3o2PZOWwh/EHN2J0pNRaq1dv9kKoK4WnT5fbUhtHR0pYOpTuI1057OHgzk147cB9OLFnW2jwFLbWjevaiIiIiIi6jzN0WYgZz+kZMJOVy5fipT3bOm4PK/2vg67JqTKuzc3H25gmf4Py8WPnsGtiOrIPnZ41DFb41GmaURUwg1U2Vw57ePT+OzkDRwOjG30biYiIiOJgQJfS+LFzkcVFCl4O1+bmMa+AvAju+ZmVOPHKJeN945b499+etB9bqVjAiT3bjA3Ad01M46GJaRQLHkSAymytbeD6pdEN2Hz7qo5BLQDjc516/RK+NLoBk1NljD05g5qvAufl2RrGnppp2x8OlKlfmb4v/dTonoiIiBYHUapbBfeT27x5szp16tRCb4aTO/Yctc62Fbw8HrirhKdPl9tm1wpeHjkBrl7vrI4pAIrDXkfgtOXAceN6NR2MAcDaPUdjb38+J/jU+2/Dcy9fjLUWUO/bcy9f7Ajkxo+dC32ulcMeLs/a2yUUCx6uzc23vWdeTnDLsqGO9yUuzqhQVly+k0RERERJichppdTmqPtxhi4lW2GQvAgeuKvUKmLiV63VUSx4KHgwlv3XwY7/iv/Y9nVtswFAZwXKvEjHa0Wpz6uOgNNFtVZvS7UsV6oYe3IGEETOWIYFcwCMvfFq88r4vgSDsbCAjTMq5Jc2uI87m05ERETUDSyKkpKtx9qn7r6trYhJ0JVqDft3bEBewhfg+dfJ7d+xAcXCjX50y7z2jy9uMOd/jSSCrxant11a+n3xMzU6f/jwmVbhmLB1iElMTpWx5cBx3LHnKLYcON5WoCbNfQfZoOxn1LHiwlb4hwWBiIiIqJcY0KVk67H23MsXQwOlW4sFjI6UnIKwcqWKLQeO46GJaVzxzV5dnq21DUJtzccHjet+BGdCogK2LGdU4gQEUfcdlCAoShZBUq9kEdzbLuaw0T0RERH1ElMuM2DqlxbWD85fBTKs4qUmQCutM3jfaq2O3YdmEvefc3n9XioWGpUug+mlJsGZEFtgpgNi234Gn8clFc+l6qjLfYHw4jGDJM57stCyCO71PnFNJhERES0kBnRdEBWoCRT2PXM2ci2ZFhVwJU21dHnutPzFTFYUPFy9PmdNyyx4eez96J0dA+VlXg7VWmc7hq3rV7f9O6zRue324IyK6zq7OAFB2H1NQZAC8MTJ89h8+6qONg/9HDwM0poy27ESN13SdDGHiIiIqJeYctkF48fOhQZKs7V552CuV0SaFTYL2aVtCoCd778NU1+4Fwd3bsLypUNtwZwAWL6kkbKWF2nN5kxOlTE6UsKJPdvw6oH7sGr5UuPzP/fyxbZ/m1Lgwuj0WP+A3DUVL876qbD72oIdhfY2FIOQzjhIa8qYLklEREQ3CwZ0XZDljEQuZtPypJQCXj1wH6YfvTeyUIvzcwL4+sybbcFI8O/X5+bh5W9U5zQFKq4zP/71jFEEwIk92zpmV1xfKywgCK6J27p+tfW+YcFOsMdglgVdumGQgiTb2lfOthEREdGgYcplF4Sl/sU1vwAL3NKkcAZVqjX8ztfs6+Fqhh0MrruKkx6nU+DC+gPaHhvntWzrp4DONXFPny639exb0WzSvmtiGitCZkT9rzkI6YyDtqaM6ZJERER0M4gM6ETkTwB8BMD3lVL/zPD3MQCf9j3fzwFYrZS6JCKvAfgRgDqAOZfGeDcDU8+4tEQas2jdtOXAcYxtX4dShgEpYG6gHqVcqbZSL1168AWFBdVhj43zWqaAYMuB48aZtOdevogTe7Z1rNGrVGvIAQiuEAy+ZlZrvoKyXpfHIImIiIiot1xm6P4UwL8D8GemPyqlxgGMA4CI3A9gl1Lqku8uW5VS/5RyOwdKcKYiqhgI0FhLFhb4dDuYA26kOz5wVylRs/GsBYuRjB87h3Kl2rbezv93v7VvNwdAy5fksem2Fa3KoHkRfOru21oVJdPOMkXNpJlSJ+cBFLwcVi1fan3NJEFtlEFvtJ5VMNrvxWaI+g2/M0RE/SUyoFNK/RcRWev4fJ8C8NU0G3SzCM5UTE6VsfvQjDGdseDlcH2us4pjUgUvHxqMFQseKlVzURY9m7R/x4ZWAGUz7OWw1MujMltDTiTTVE29LTr1Ur+XphL/T546j9d+UG0NLrauX40Tr1wyPue8Um1/qyuFx0+eBwBrUBcWOAZFzaTZAr5qbT50UNSNdMZBajMQlFUwOuhBLVGv8TtDRNR/RDkMwpsB3ddNKZe++wwD+B6An9UzdCLyKoDLaNS/+COl1FdCHv9ZAJ8FgDVr1tz1+uuvu+/FgHhk8gyeOHm+bW1XwctjmZfLrOrlyuFGH7eoYCzKYzs3ha5FEzSKqGiTU2X89sR0R+pgWgLg4M5NqffH5XV01UnTjGrByzsVzQgOdoKPHfnis9bPulQs4MSebZnsjwvXz7YfbTlw3Hg8xH0Ps3oeosWC3xkiot4RkdMuS9ayLIpyP4ATgXTLX1RKlUXkJwF8U0ReVkr9F9ODm8HeVwBg8+bN/dTrOhOTU2U8fbrcNoAWAA/cVcITzRmiLFyfm28FHWNPzhiLjrgYe2oGgH3GKSeCO/YcbSsGks8L5kPSSpNYUfAyX49oolsBADDOXrrOXIXNpE1OlfHjt+asj41b4CQs7cklJapb6/Ky2P4oSYvEBF/TdpGgn4rNEPWTQSjQRES02GQZ0H0SgXRLpVS5+f/fF5GvAXg/AGNAd7OzNZB+7uWLmVbF1OvwRkdKsZqXB9XqCnuPnMW1OXMgFWwzsMzLha4R1PIi+InCkPN2/fCt2oJU+jSJGywcbM5yauPHzoUG2KZAyhb0hKU9AZ2pqaaUqG6sy3OVNm0rSTBqek0BjLOU3Qhque6IbgYLfSGIiIg6ZdKHTkRWAPglAH/hu225iLxN/zeAewH8TRavN4jCrmrGbYjtqpIyjbNSraFai06irNbqzgHavFJ49P47nfe3X4I5wC1YCGv8HRUQXr56DSNffLbVv+6RyTPW5wxb/+bas24he7Gl7auXpOed7aJKsOtiN4Ja18bwwR6G/dQ4nggYrH6TRESLhUvbgq8C+GUA7xCR7wF4FIAHAEqpP2ze7WMAnlVKXfU99KcAfE0aTaqHAPy5Uuqvstv0wRJ2VdNUxTFpgRE9OJ2cKnelUElaOZFW/7U2rEnuAAAgAElEQVRlXg6V2Vpov7h+4eWlbcASnG2ZvT4XWWAkaiZ2tjaP2WYAXa5UO9Zb+p8zSdqT6W+64Izen10T0xg/dq7rs0dp07aSFImxPbdCI5jt5syZSwEaFpugQTBo/SaJiBYDlyqXn3K4z5+i0d7Af9vfA9iYdMNuNlHpbf5KjqbCGq4+fc+a1uP7LZgDbqRqVqo1FLw8Du7clCo1NKmcAPmcOKWJAkDdN1VoGnjb+IOIuP0JbVsWtv5rRcHD8qVDsVKi4gYSSVMH/Y+zXWyIk7YVt+ed7T3rRTEHlwB2kKuO0uLCfpNERP0lk5RLihYnvc00sHOxfEkeT5w8j92HZha8h5yLaq2OvUfOhhYK6ZZ5BedgTt9/3zNnAcT7fPwBiv8YSEMHUV4umCwIXL0+h63rVxv/Nnt9zpjCFyf90TV1MOpx5vYd3U3bsqWKbV2/uutpjrZA1X87i00QEQ0WpslTv8iyKApFcL2qmXQApwui9OPMnI2tH14/0rOIcT4fHUTpz10fA7aWAVEEaAU9c4bPuVZX+PrMm50Lw9DYftPMW5xAIs4sksuMXF4E80r1JG3LlCq2df1qPH263DY7OfbkDPY9cxaV2Vpm2+VSgIbFJoiIBgfT5KmfMKDrQ1lWvUwjJ8CyoVxrXRc1FIc9Y4qoqWLi5dkadk1M46GJaZR8wUHSz1gBOPX6JeP6Oi0sSNazov6TTZxAwjX4C57obBcZ5pXqac+74EWVLQeOdwSotXnV+nyDJ+ik6aYu644WsuooERHFwzR56icM6PrQ1vWr8XiGvemSmlfAyuVL8dKebaENsZPKuql6txULXmgvOVuApW/3Bwdx19P5pT02KtUaHpk8gy+NbsDkVBlXr3Xujy2QcA3+XNNSXWafulnu32W21Z9+muZqbNQMPYtNEBENDqbJUz9hQNeHnnv54kJvQov+YbqWwZq8YrNgh3+wGjXblBVbvzFXOQH2fvTOyF5yUaq1OnYfmsG8Uljm5ZCTbFszuAbJupm9P91QWzns4dH77zQGEq6zSC4nNJfZp26ntLjOlF6oVHtyNZbFJoiIBgPT5KmfsChKH8rq6k7ByxmLY8Rxa7GAyalyJmmXez96J07s2YZXD9zXqir49OlypsHcknzn/roEc792z5rQYiU/sczD6Egpk8+mrhQUgGptPtNgLi+C/Ts2OPX5UwCeeP68cRZNKXuw5Frcx3ZCy4vE6nmXtl9dFNcekCsKnjXwS3tMcFE9EdHgYU9G6iei+rCAxubNm9WpU6cWejMWzJYDx1OvofNygvGPN7pG7D1ytrWuKu6M0PIleSwZSp8WuXQoh2tz860ee2l67YXR69R0Tz9XujhH2BZ1a5uzIAAO7tzUCpAemTyTKjXzMd9zJWFqvVHw8rEbl9uKxwiQeO1dMIVz6/rVeO7li7hQqWJFwcPV63NtFVC9nABir4qapu1BVu9TFrqZ2pqFft++Qcb3ligZfneo20TktFJqc9T9mHLZh8a2r8OuienEVRCDPyr+H5c79hyN9XxXr9db1TOTyglwba4xw6cDoqSBUVRKYblSbe1vnPfQZXv6OZj79D1r2j7noy++meo506YRxlkPFnZCdE1pcT2pmlI4J779Bm5Z1vgpXL50CB/Z+M5WgKebxtuOt7RXY/tlUX2/V2vr9+0bZHxviZJjmjz1CwZ0fWh0pJR4bVnUFSJbhcZukIxfT6/tAsKDtW4UcEmi4OXxvjUr8K1XLnVtjaBtvVva/S9Xqthy4HgrWEla2THqfmGDSQBOBVtMz7FrYhqnXr+EL41uaHusKYAKVrV8+nS5bYYs7CJI2pm0fllUnySw7OWV6X4JfG9GfG+JiAYfA7o+9aXRDdh8+6qOAdO+Z86GDtbDrq6GVWjshpxIpoHV5dkadh+aQV0pFLwcqpZ1fVm+ZlSaZcHLG9eh+VM/uxHM6WC5MltrrSfLevCle7L50w11sBRswxCXDgZMs2+6tcK1ufmO9zYnwPvWrMD4sXPYNTHdmkEL3k+hUfhl8+2rnHrumV4/apawVCyk2vew/ny9XlQfN7Ds9axO2sC3n9Ki+mlbgP65qEBERMkxoOtjphmOXRPTkY+zXV1NW6Exrm6kKOrntAVz3Xo9m/07NoQOzlw+ryRyOQntlZa2qqdmOl5MbRj8+xw1YDWtGwuy9dKbV8CJVy61/h22TlKhM3XUtaplpVprNYTPsj+cS3++hVhU75LaGhWIdnNWJ001uX5KKeynbdFYqY+IaPCxyuWAcT3Jmq6uZn3FtVkrAqVioVUlUtCY1UqqVCxg5bCX2TZ207CXw+hICSf2bMPBnZsANAI4f6VC18+rWIi3z/V582BaDxhNwZyXEyxfEl3RMY5gxUn9+uVKFQo3ZvlGvvhsq4rjvmfOJuq/l4ROHdWvvXX9aqeqlgDaZj5dKnu6Pqdp3+NW/0zKVlEzqlpb8HO1Xejo1qxOmmpy3a6UGkc/bYvGSn1ERIOPM3QDxrUhdU6kNcOguaxnC1ahDJvpmVfmaohxC6/4ndizDZNTZYw9NWOtKtgv/u2O92JyqtxWRRSI30C84OWx96N3RqbTRrH1SgMan+vO99+GiW+/kfj5bfzr7VzWqEXxcoJblg1lkjorvtcsV6qtdan6+C4WPOtsoA5ObDOOcVPnJqfK1v2fVypx1U5XLrNDtv3JslF8EmmarvdTSmE/bYv/+F1R8LDMy6EyW+uLNFAiIoqHAd2ACQ5sTGXWgcYV9GAansv6uWAVyqiQauzJGex75mzbQCAsra1ULODNK1Vj64S2eb0YsZxuOQBp9FDrlYcPv2hN/dRX3U/s2YZTr1/CV59/oxUo3/MzK/HaD6rGgalLsG6jZ8RM5pXCcy9fjEy59fISq2S/Vq5U8VBW6aUC3Pfedxqbnkc8rO2wMV2M0P/Wx7dIozWHqZKr7sFoCoJOvX6pbfuiUuf089j0Ir0tqvhFWBGbrBrFp2HaPpeg2vZ7lBPBHXuO9jSA6Zf0xuBxXanWUPDyba1PiIhocDDlcgDpNL9XD9yH6Ufvxc5fuM14P38qj239nDTTJpOmSerZFx1MPHz4jDGtreDl8djOTTixZ5u1D54CsHbPUew+NBNrrZ+e3Tj4iU2J9iGpqHV8FypVTE6V8fTpclug/K1XLmHr+tWtBuv+2Z5qrd76LIoFDyn7wrfkRCJnx4oFD+MPbmxLLdz5/tuwfElvr/vU6o3gc/+ODbFSUQ/u3NS27S5H0OXZGqqWthxb16+2BkFfff6NWKlzYTNctkAo64bjaWaHogKOvAgeuOtGwNWLZumm9N6HD5/peC1b8/h6s++k7XHd0C/pjf2Y+plEL44zIqJBwBm6AacDBhs9WLMO2lSjQXOaNEm/aq3eGozb0tSiKkfGLaaiB5ujIyV87vCLmO1RwZQotxYLxoGTAvD4yfN4/OR5lJqNrf2zPY0qntFpmCtjtISISp8FgKvXGzO4ukm2qXhJlumQYS40+wn6g92wgFRXnPTPLmw5cNwpxdN2tOhedCZx15CFBU37dzRaK2w5cLyt2XmcGUAXaXr6RaUO15XC06fL2Hz7KgDoSeEP13L7wayGXhd08UuTOpqlfkr9TKofC8wQES0UztANuKi1LXqwZrvCHvX3JPRgPFgs5Oc+/w08NDGdefVLvYZrcqqcqvqlAHjtwH14rDnTo29Lavb6XGRAodd12QamlZDAaeoL98baPoXw/anVVdsVett6uOElQ10vXLPCNzOnK03aipnYZjjCHuNCD7hNbDPaUd+zIH2cBWeaHg85JpJymR2yzXoBaCsMY9p/vX29mv2JE5T4sxrme1zQJWxb9Ax9r8U9TvvRzTLLSESUBQZ0Ay5sEOIfrEUN5lwHv14+OoTQg4Lg4LCbrQb0wLOYItDwz/Sd2LMNK4e9VKX/XWexbK8RFlCUEgbiyvdY22ua/tuvXKl2fYbu6vW5tvQpW2XMvIi1KqSuTpk0nVinqQYfXfDy+NTdt8VKnQv7/rkWHAHSBR0u1TqjZr1cgqJezf6EBSVhqXg3QzCTVr+kfqZxM8wyElFvLIb0bKZcDrDJqbK1MXFwoBuV6qP/f9ehaWthkbwIxh/c2FaQ5Ydv1drWxHk5aQ0K4gxUs1Ct1bF0KGdt9h2lXKni5z//DSz18l0PWFzozyisB9rY9nWxKoKWigWc2LPNmo64ouC1Uv9sx1ZcOYF13aRNra6w+9AMTr1+CV+feTOkL50KneEYHSkl7gXoLwyk01X9zdQ3374qst+e/7sivtB95bCHR++/E4Bb5U8tKugwpUsCaKvEmpPwiwgut0elb/ai8Iftu7F1/eqOVLyxJ2dC07EHLZhJq19SP9PolwIz1H/iViCmm9tiSc9mQDeg9AFqa0xsmrUIq2Kn/x42+P3U3be1PYduLzDvDyZ80xlZXil1DQoq1Roe27mp9WMeNxyZrc33xRo8ATrKxltPTo47qQetk1NlXL3WWfE0h8b7pwf+aYM5f/DzyOQZPH7yfKzH15WKfIzL4M21mXgYHczpthr+9W6myoCmKoJ+b9XmW5Uy4wgLOkwnrbEnZ1BXqu27o//bdFJzHSRHXWhwbcSeZuBl+27YUoVthZZKIa/bjYFhvww2o84H/S7qGKTFabEM3smd63rrQceAbkCF9RtL05jYNqAreDl8aXRDxzYEZ4b0OqzRkVImA2lt6VAOgDjNvI0fO9caJLkWxug3CjdOPmEDL1v10qCSb7bGVNxi2MtZA1lpTk+5hHd65ik4UxU3cHE1e30usvS8a+/GKLpqqctgIap5uq6UGSdoLha80O+1LZAJEzypuQ6SR0dKHe04/FUugfZZwWVeZ3Z/3IGXLRAK3jfOjKwO0k26MTA0PeeuiWk8NDEdGlgutH4JQv3CLnb14/ZSbyyWwftileS7vVjSsxnQDSjbgRiVghbFNqDTlfhctkHfHjWQFgBLhnK4Nhc9I/ZWbR4Hd27qaOJtEmzsPfZkvDYI/SBsnZufyw9SXqTVdHz2+pzx87g2Z39/lAJ+7Z41eO7li6H9BW0/rN1MvfU3LDcNtoPtINLMOtqqlgYHC5NTZaeU3bjbIoLQ4DXpyUkXFdInyAfuKrUqfNpey9SOY+Lbb+Doi2+iMltr9cfULs/W2vrw2aqW2gZecYKrOBeSwt6zbgwMbVVvgd7NJMQdEPXzjIetN2G/bi9132IZvC9GSb/biyU9m0VRBpSt+EfaA9SlcELUa/mLi/ifq1jwsHLYaz3vwZ2bcN0hmNPPOTpSwvKlbtcg/NXOgo8Z9nJdqdJYLHipqipqXk5aM09Ri3ddPm9/vy1boBEVXDx+8jzWvr0Q2l8wTVPqLAQr3PmL8gA3WjcktXX96sjBwuRUGbsPzTg9X9xaLcF+j8HjIs1331/V8omT51GOCOZ2H5oxzgbqbaxUax2z99VaHXuPnG37TExM73GcioZxqpuuCOlz6PJZx11kH/VdMB3DWS7kd+3d5zdo1ST7aXtvhkIMg7YPLHp080r63b4ZikC54AzdAJqcKuPHb3WugfLykskB6rq2wiU9K+q5ovqLBZ8zTnCgByvB7fu3zQB1cqqc6ezdRza+s6NQxtb1q0NntkzqSrXNPI091QgQuplO6DJ79a1XLuHTzZm6qNkb/3tQjNEvL61gYBXcpzSf9HMvXwy90he2rjXIy0kr0E7CNCtoWxcZd0Vo2IxRnH00iZpdB8wDr7CKq8FZS38qXtT3TldTtV2wivqss7pS7Oc/hrOeaUoy6xj23tveu4Vke397PUNzM8wUJt2HhUx55drKm1fS2deboQiUC1EZ9wTLwubNm9WpU6cWejP6lm1dWLHgYfrRe3u6LWl/uMOaV1dmax3PGWdNnC1I8a+bmZwqO6Vxur7evFJtgZx+X2avz6UKaoa9HF763Q8Z/+bSeDvKlnevwolXLkXeL2zNkd6W4OeZJKhIo1jw8KNrc6hnnGYrAA7u3GRNSY76DPRaxCyOB/826fRG10qncfk/826vSbUVdIrzuv51nKbjMchf7CZ4Mcbf3N2/fbbPOsn3w/Yctn2Oeo3g6/n3yfYeCoBXD9xn/FvYex/8vPy/Rfr3t5drAyenytg1MW28UBLnfctCFp/fQkuyD6Zj3Pa97pY04xKuv+xfN8N3KgkROa2U2hx1P87QDSDb1YgrGQQlQVE/bmkrpcW9cjK2fZ31hO0X1rrA//75tz/tYFUHj7oxtJbFAHi2No+f//w3WoVL/INWvf0PJSzPDwAvnL+C5UvyuHo9fKbP/96Zjg3TDECva4ZmEZybKDSOU9sas7BiHMEBzR17jma2Td3aX82lN2FWlnk5nHr9UsdxFWcm+vJsrTWrDTQKKoU9zlbs5omT56Fw48JQseBBBKG/P3GuFOseh8Hnmm3OGqZdC2TaJ9PrAeHpaGHvvX92L/h6/t/Dbs5M+X+HciLG/dNVg3vpZljLlWQf+qEoSdJxyc0wq3oz4+xrOAZ0A6gbCzxt/avS/rjZnjd4m+vVldGRUmTgkhcJLYJhe59cBo0FL4+cIDLw6QZ/FcrgoNVfcCKJaq3eXAOI0P0PNo0PHhu97DvYDVvevQovnK+gaqn4Wa5U8fTpsvFqs+17aao8m2UF2G7zf1+6vd2XZ2sdF0MePnwG+3dswP4dG4xptCa1usLeI2dxbW4+8pi0FbvRr1JXCl5OnGZBXX6Dg61fghkCuoCMLVXZ9hrB5zK1evH3VNR07z5/YRxTj1Lb764e3IcVP+rWgN4WRAb5qwa7PGcWMzS9KsTQzRmlJPswyIFsPwSjZLdYUieTYlGUAZT1Ak/bQnlT6fU4i8tNzzv25Ax2PznTfttTM8aF1rbF2LYKkLooib/yXlDY+6SLuIR54K6ScyGXbqvVFXYdmo4ske/qSrWG/Ts2IG+p1uG/ym078Q2qlcMeHtu5CU/8qw9g1fKlofet1urYfaj9mLWtYSt4eXz5Exs7TjhxCnd0i5cTePnwyizB78tCbLd/QDUfY4lApVqLPCb1/kUNNmvzyimlVc+uubIVeqrW6lAKzr/zej2wPzC0ZRzrnoq6ONUDd5Xw9OlyaKGU0ZGS9XdXD+6j3sNuDOhdK+i6Vg1OUjTGpheFGLLcXpMk+zDIRUkGORhdLEZHSjixZxtePXBfaDG2xYgB3QCKU4nShW1wblvjo8ucR1W9svXFCq5tqtUV9j1ztu023bTcFPjZTjIi5qAiL2J8n0wBY9jApVQs4LmXL6YqopJLU2bRQClkVnBkRbPPmW3Q7L/KPSizS66Glwxh18S0c9ptXanWwEkPqoKpjyuHPev30v8dBpCq+mYSeRGMf3wjxh/caA3gTTOLwe2OsnxJNsGfHlBlNSgM/h5k9bx6di3OgDoshd71d961HyVwY72JHhA99/JF4+//7kMzbb+NUYP7qPewGwN6l4F2nCAqqwqZwXYpQPrzdDe31ybJWGOQKwoOcjBKxJTLAZV27Zpf3KtPghsD+rA0zDjPGwxK9j1z1ti0/LcPTeP3PrGpVZjAP+1uW8Oky9VfvTaHfc+cxa6J6Y5CEsHedbY87ThNi01+YpmHK9VaqmqL3aLH9bZULz2In5wqW9fipO31tlD8x7Nt34L8AyfThYThJY2f17BUtqzWb/q5bP+n7r6t9dq2Y1oH9qbtHx0pOW3z9bl5eHmxzm65Hi96QJVFVddgUSS9H66fu59p+/3HhUtqUFham+vvvOuxYxpY2x4bXAOn015t+xT22XRrQB+W5qwLVMVJycpihsaUBqr3P+vZhG7NKKVJ4xzktDiu0aJBxoCOrCfFYsHrWINiGvTYcszTrLexzTrNq8Z6sQfuch8YAZ3FI0yFJPR+6MFesOLd+LFzqQOxK9UaPn3PmlbBhX5Sma1FtsTQ7QBs2750SDBbi7dnprU+CynOpoQd38F1hVld/AgjQOP4ev48wuKk516+2Ppv2/dmRcEL3X6X4Ko23ygmAnR+57ycYOf7b+uoJBnkH1CZBotb16/G12fedCoO43+u4MDbv77MJbgT2NdslSvVtjVnYZ+96yAyuEZOF0bS22Lb3qjgxiWo9v822gbmwYIvvahyaXvvks6EZbHurZfrsLq1nj7t2vksLzj30iAHo0SRbQtE5E8AfATA95VS/8zw918G8BcAXm3edFgp9cXm3z4I4P8BkAfwn5RSB1w2im0LeiuszDDQ/uMWp+x13D5vr/kevzaiCqBpYb9eC5J2LZcAHT3kkly9N7GVSM+qjH3abQPMQYqXA+bms3kP0igWPCxfOtRXKZ+2Y0MExqDKVGI5yxm6YsHD1Wtzod87//fV9v1f5uWsM7X+Ga7Idg1otHsYe2qmbabOywvGH9wIoDNA81cR9f97RbPSpKmlCRD+PurAQr9e2DbripaXZ2uZfff18y5fOtQxWDS1TAi+BxPffqPjM/XygluWDll/O/R7HNYr0nXfwlob9FLUe5VmAJ5Fyf079hy1VtrM+v3rRouAxVoanqhfZdm24E8B/DsAfxZyn79WSn0ksAF5AP8ewD8H8D0A3xGRI0qplxxek3oo6qqUy4DJekXQcXGQvorv/3fYFXfTLOFXn38DdaVSp/3pNXv+anuuzxY22+Rv/B68gunSnyqJOIPRsKDSUvSx5yrVGpYvHcJjOzcBQFfes7hs76/tEDR9f7JqEA/Eb95tmlmp1uqZtf3QVSRNKdR7j5xtC3BMgYz/Io1/30wzB7aZTgFaF1Jc3udKtYaCl8dKS/pxUpVqrbUPwe0PNm/3z5D4f4v8anUVun3+YM4ffCcJUvthHZHpvdFVZ4HGMbxrYhrjx84lCuyymKHJetYsLP2xGzNK3S4Mwj5vRN3h1FhcRNYC+HrIDN3/bgjoPgBgr1Jqe/PfDwOAUmp/1Otxhq5/xbki6DrroFOvXK5IuwqbYegm/wzcw4dfbCt/vyQvuN4c1Pr7yGlpB1yLiX8G2bWUvYmXE0DQFmx0+9jJi+CV/R/uuD3JrEkStu+ra7Bju1Jvenza49jl8S6Nz4sFD9OP3tv1xuhJFLwcVi1fmvlsfXAmNc0Fg143hrYJ+3yDywMWapvDvgdx00/TzMAlDZy6OUPXD03H6eayGC4QuM7QZRXQPY3GLNwFNIK7syLyIIAPKqV+o3m/zwC4Wyn1m5bX+CyAzwLAmjVr7nr99dcjt4sWhusXyJZ6ArSf4IJX4YEbKZSua2NMTCf5btNpNS4DKFs6FND+HpvSzPY9c3bBUzR7IS+Cn16xLHSAkXSwGkzBC/ZKjJMuHNdjOzdZv0OTU2Vr82r/eqi1by/gxCuXYr2uXmP3pdHO9hwuwU7UwPSRyTM9Xx8aTB/t5ufWj1yCmTSBrAhw8BObetbHLezxYecUE1uKa7eFXZyLE8DYPreodZFpA8Gsg66o1OyF+pxosGV58aSfZZlyGeUFALcrpX4sIh8GMAngPXGfRCn1FQBfARozdBlsF3WJ64LnqCIlekC+5cBx4yLy516+iOlH78XIF59NFLxcqdZw0DdwXlHwcH2u3tagO2vLvJzz4KlWV9aF8mHvsa1wSb9Kk7ZWVyoyBSiYMhjGFtCY3utuBc3FiGIjtuI7AnT0tLvzC38Vq8m9QntBFL+olCr/wDS4zaYG2b0STB/N8nMrFuJVpfVy0tNg0ssL9n60URglLIhKky4XZ/I7bUEN0+Mfmmj02nz0/jtjF9oKS3HtprBKsHEKpNg+t2AFUv2aWprCLFmncbpccFuoz4kGm+k4t52jFoPUAZ1S6oe+//5LEfkPIvIOAGUAt/nu+q7mbbRIRK0L0ierqAF7JWRwJgByljVzwbLf+sTSTdXafKwBR5KBVpyeUy5sAVfBy7WljCY1vGQIlVn7oLgUkmYWVqglJ9LqHag/Y9PxpgORvAg+dfdtxtmpoMmpcuygIHglPgfA9O7ZKqzuPXIWoyMl6zGhAOw9crZVPXHYyyW6OGF7fttg2fb5+Mvzx50hzaq9hW6hsuXA8dagM+z3Io6Cl8fej97ZVq3SpFQstA18ezl77p/hDxu0RAVCUWnGLoGAroBra+PgMqiyNQq/PFvD2JMz8PKdi7LjpEibtqWbKVtp16O5BLD+fYq6sOL6ullWqXRt/u7Xraqg/ehmSRns1n6EPW/U8byYjiMgg8biIvLTIo0OViLy/uZz/gDAdwC8R0TuEJElAD4J4Eja16PBMTrSaEpqa1ysr6xHNfO0/b1ULODVA/fhy5/Y6NTINMmJpdviLpSfnCpnugaoVCzg0fvv7Hj/BMBbGc1k6ubwttc/sWebcRv0Z2hqVAu0N/cG7J+v8t3/iZPnsdbXLNkkaeCvZ50FjZmdvGHwGaZSrWHki8+Gzgb5B2pJZ5p1IBxke58vX71mHSxfqFRjfa+KBQ8FL58qmNPNyk0zhpNT5djfqXyu83NavuRGillYE/W8SFuT7tGRkvFYjqvg5SLrSZWaF6xcjG1f11gvarBy2MP+HRtaLRBMogZO+jsT1sZhy4HjbY3K475ObV51HPP+bXd9z/2vobdb/0b5j6MspG1UbftOBl2oVFvpxmGz5Kbvvu7FGPXZJJV0djirIiz9rNvHX690az+intfle7QYjiMtMqATka8C+G8A1onI90TkX4rIvxGRf9O8y4MA/kZEZgD8PoBPqoY5AL8J4BiA7wI4pJQ6253doH41OlKKDLhMJy3998mpMq5e60wvDPam2r9jQ2swXSoWjPn+Sb7YeghUKhbwa/esaXuNtPxVL7Wwk6troJEXcSou6m92+8BdpbbHKHS/KIsA2Lp+NYDwzzDswkC1VsdDE9NYu+eoU6BrCgCC0gb+B3duwvKlQ9Zm2mF6MbMTDIT1MbdrYhpLh3IY9tpPC2GBY3HYi/W9qlRrqS+qXL1eR17EWOl275Gzxt+LMHXDbPfV627H1afuvq3jtuCxXCx4WDnstf7bEle1NKhx1IsAACAASURBVNYrvRcHd25q/c4EHxLV7HhyqoxN+57F2j1HsXbPUex75iyWDJlP98NLhlrfs5XDnvE+UQOnfc+cjfxcXQZ7cYNx/7YHfz9c9sWWmvjQxHQmwU3Yuc1FcL/CLo66ZG6YvvvdDijCPlPXz2lQxA2Ow1JjB0m39iPqeV0ueAzicZSUU1GUXmOVy/6SxVR61HOY/g6YC1OYKkS6iFsYwPY6Lr234jx3VHVL3QrBJU1NF5PR/fNsgk1/0+5PUl5OcMuyIWtfMb+4BRFcmCq3pX2dQalQqj/7NJUp+7EvYC/kBPjVu83FZTTbb17YmmDbQn7/b0RYw+4kaxldehLaCmKkXTvp34e0227atqh9ifquZ1GB0XZuS3JODdsnWyElE//69TgVLZOMBWxr6PQ5EOhM2e5G5ctupzYmKSbTy56F3dSt/XB53qwKEPWzXhZFoZtY2kXuWlROvunvm/Y9a7ziqFSyRa5Ra/qiKocB2VTxe2znjYpxwffX9Lz6LYgK5mwVQ/2CZfp10YGFUpu/0UfLdGz5T8K2tZJpmGaXiil7jw1CMAfYP/s423+lWsPej97ZF/0Ae6Hg5fDd3/2Q8W/ByrRXr8+1fc92TUzj1OuXQtf4lStV7Humkcji//0xrRHV35dTr19qXcBJ218uTkGMLHpnlitVjD05g88dfjFRCnHY1XeXfYlao1at1bH70Ezb8wFuwUHwPgebv/tpzqlh+xTnopzr+vXg/iTZbv23YLB+ebaGhw+fwf4dG7B/x4aeBlvdKJiRpBBNVM/CQVlfl3XvxTjPG6yTMAjvV7cwoKNQaaplpWW7Upv0arDeXlsAM69U6NWkyalyaDBXKhZQmb1urTqoKyxGVSOLS4DWYMFUMdS/fVn1uNLCGqknEVzg75+hzTqYAzpPOINWQXSh6cJDgFuV0UFXrc23CvH4BQeMpt8oBVgbhPtdnq1h7KnOIML2W+z/TYr7DfGntgfbpETJak1ybV4lKvLkkroYdSEx6iIf0Pjd8X8eLsFB2H3SnlNt+zS2fZ1zyw7/+nTXgbgprTZO5czxY+c6vhf68Xr9abf0YhyTpACO6fjzfye7HYRmJWw/evm8WRbzGUSpi6LQzS1tla5+E1bkIOpqkq2cPNAIqk7s2Yb/82MbjDndOblRMt6fV5/F+6ia2xb2fHr7sqoACDT6U3WjQrsuoPDQxHRXS8B7OcHlq9da64xGvvgs9j1zdlH1MEsjuAZybPs66xqfm4lpXUjWBZdqdYW9R9qXnIdVP3VV8HKtzygvggfuujHzp9dRVao1XG5WpS1Xqhh7asa4FqjX54BiwWv9dudFWgPyNOu9/GvUwtTqqjVz6rJeKOw+3Tin6oC8Nq+c1k+7rF8PPn9YYSQXWe13kiIuvRjHJCmAE7Z2PO26tG4Xu/EzrRte5uWwK+VaVNf6CNTAGToK1a2pdBe2cvqmRdRxptqTXk0K+/HX70cwJcaUeuW/yha3r1LUtkV9Xlm9HhCvP1UcuhR9N+i02hUFDz98q4Za7cZO3OyN2rNe26dnnI6++CZ+/p1vw7deuTQw6aZpmH4HuhHcVKq1ttnAtN/dnABz86o1011XCk+fLuPoi2+GBqM6mAn+nsbdnjTHn24hAZjTToHksxa2lMAg/fvgEhyE3Sfrc6opbT+slUOx4HW0uvCvz/QHDf6/27hudxb7nXTWqhfjmKTjCtusUpogNLg0pBeze3o/sp5ZXOyzbnFwho5Cpa3Slcaj99/Z0XfIy0tHee24lbqSXvWx/fgL0PZ+jI6UWqXMTdUO41ZpcqG3Lerzyur1THR1zbSzNN0MCurNtNrlS4dizS4WCx6Khc4LCVnPR3VrgisvgoM7N3XluS/P1nAiQTAnuNGCoBdMn5+flxM8tnMTtrx7Vej9TL8D3brA5R9Im767rofLymEPKwqe8bfI5UKG6T5xfktWDns4uHMTHtu5yfkx/grDWc1a+OkZjLV7jmLXxLRzKn/YTIx+Ttt34dbmOue4VUvD2N4TpWA8XirVWtusiZ5d97cTCZ5Do9IGXdjOTVvXr3aeSUr6+fdiHJP1bFLSlhe2pSG9qp55s1TuHEScoaNQcRbJL9RrJ8mPT3LVx1YRMLguzi/qKptpH7euX42J77zhXPY+2MIh+Hz+KnL6vepGJUa9BvGOPUczfuZOpmpWD9xVCi0IA9wINuPMqOjZAf97qGdfr8/VE/eDM+nWrOeXP7GxO0+ckK5uCJgr2YbZ8u5V+Parl62PCR4b+t8/ClkbmRfB+Mc3toIGG9sgMM76pTj8x6lpJsnl1QTA1BfuTf299Ddv92+PS9Ei3dMyKoNBs1UYtn1vy5Uq7thz1FpFcuv61Xju5Ytt//b/Vri8j/qCgG0mZuv61aHr8XTmgWkd5QN3dZ6PXLNObO/JlWoNB3duMlYADM6aRJ1DbTNc/tm+KLZznf9zCBb6Ce570lmrXo1jspxNCjvOthw4bq2amjO0dNFcz3tpiov02zKdxVQohQEdRVrIKW+X1+7VD0iSk0KSVI+jL75pDOb86YIiQGW21vrvXRPTGD92znrF0ZSW4+UaAzDTGNSlRYJtn7JM67RRuDFQ12lCz718MbJlg96nsG3UpfhNn3FnWkl2wVyYNAG4HogmaZbeDcWC11YSfd8zZ2Olu772gyrGP77RWtxIN3gPDmLDjucvf2Jj63MNO3ZtV9xHR0r4na+dQc1SECkp0+9E3D57WX0vdVXUzx1+EUu9fKvViA6WoqpF6uDA/x3ShTLCWjEE98X2Oq01f0/OdFTy9QdRtqAqykc2vhOAOSgViS54E/b9Pfrim20tMExpa7pKarBVRtg5Rr/XpvYEut9eWDEjfQ61BRc6DdZV8HxuKuIVLPTjDz7TpE6GjSVc24KkETewcA2Ax56cwTxu9NMM+51zeZ/ipkwG98tWJXpFRIZEN8T5Ht0MGNDRwOvlOr+4wW1UXr3pB8cmWIXT9FjTYObhw2ewzMt1nDhr8woFL2cMSu75mZV44fyV0KvNwRkyf1pnL8rY66DOnyb09OlyqwS2rb+S3kbTjIqXl9ZsXNAjk2fw1effSFVtM0lgFmw1EYcedGVVtCOLmV09u6QHAnHXLl6oVEPLtOuA0bXvpEij8m2cPl7BQczatxes1W2TMs0GujSPtj3H2PZ1sfbRZrY235qVjhMclSvVVjpdcL1aXanWtvq/e8H3OaotC4CuFTV6+nQZm29f1RGUZvFbF/wOmL6vCsATJ8+3tkFzWbsVdnEzrN1FTqRtHWfWMx2uhX70BQGX82ncnn/BzzCYdgqkX3eWpuVDVADserwHl4bYxMl4Mu2XlxNj9eur1+eMFYK7Kc736GbAxuI08Ey94fqpoaTtytzkVBm7D804BwjBZq9xG6XHoa9OmgoFeDmBl5fWoM6UHhXWJHjYy+GtufmuVMj0b7utYbY/3c+/jcH98H9uyyyBb1xeDnB9GkHjokTcVhN6P4u+mdws3mov13jmLCYlH9u5KfFAWH8Pgm0t/GwFlbLwnp9cju9dfqurFyzyIq1ZQz+XpvfB49z/HGt7kA4dxuXihP/7aRq861n4C801071ULHiYfvTe1r+z/A3W3/eowNvU9Dtq9sdlO21BXdJzadh5L0lvUd1MOux5g8eL6XgL7k/Ue2Nrsh5H3Abufv79TXq866UhLrNSYb8xwd8U236JmJcPuL6XWaVJRu1L2s+1V9hYnBaFyakynj5d7lgzY1qTsFBMs3r65ON6MjNdre9mTnq5UsX4sXO4Uq21BQZ6zYt/3VhlttZK3dE/vKb1Pv6AaeSLz3ZtwK1ncIAb1dtM60f279jQNjgDbhRK6ExPyia10vVp8iJ4Zf+HASDW2qeVwx7ue+878fWZNxP3awxuR10pFJtVQbMKwpPOGHp5aVsvakvXvDxb68o6UQD42+9f7cKztptXyvj75ZI2qYM506C/W++JK5fZBJ3aabrKX63V8fjJ8ygVC/j0PWtC+4J2Q7DyaJa/wf6CXra0NdtrRmWObF2/2ikl1JRqX63VsfdIZ5XTMLYZqVOvX2qbYTWd/2zHqC46Yxvom35TTMdbcLYp6jPM4jNOuiwk7Qyw/yKB6+c3vCRvzTYIzixaZ1gtX0qX9zKrKpmTU+XQCwaD2norDAM6Gmi2KfXnXr64MBvkKGpAG7aOS4uzJqbg5QBIx9VuW2lrf+uASrWGgpdvLbAPBgqmtQ5A59X1y7O1Vi+nJP3w8jlprRPQ22g78QM3Bjm29SPBFJKs0qey4D8JxfmclUJkSlrc7Sh4+dCeg0mChKgr4q4zqGEXBfzrLAeNLV3ctfhKcLCiswEG6b0I28WoVE8vL4lSlDXbDAMA7D40g10T06HrhdKo1upYOpSzHrsKnQVqoricD0vFgnWQW6nW8POf/0bb2smw17el7dlS1vX6cFtaranojGtwYeK/b9TvaxZLN5IuC0mTKh+cTXYxOVWOTB13KZZjW4Nv29+oWdu4TeBdLpj3ovVWrzGgo4HWbxWVXIVtn7+qoqZnjvwBnimt0FboZG5eYecvvKujchjQGXiZBhL6Km3UrI+/PLHpRHR5thZ5BVoLLk4HwheIA+YqYFEL/rWsm0OnIUBrJiDOmsQsZuWCqrV66Gv7i5CkFZxZCiti4FLkJctt65WwcuqjIyWcev1S5GyLf7ASNxvgZjD+4MZEaX1a2N39a6xs64U028BWmj+ytpe5Uq2FzkDq9dL7njnbVhzLFmxFnQ/1MRdWICW4djJs1iSqKFVQcH345ttXdczEJa3EaeL/foT9vmbV2qAbvW/DeDmxFqyJmuV0EVUsx1Rx2ra/tjWMttd0EXUu71XrrV5jQEcDbSEbn6cRdmVL5/f7K2/ZUgZ1ARD/j7MpDa1WV3ju5YvWnHH/c9hOiq7BQtQPb7VWx1sOwYlOOfQLDh6CJ35TFbComTzX7e4lBbRdkZyr90egaVLwcrErL5q4LtoH3IPvvEhffa4uwtYr6RTzMMH3sZ8uVPRCyVfdEYiXshxXbV5Ze0cWCx72fvROY7BQLDRmm20BlM4y+fQ9a6wVRGvzqvU77/9tNgVbYb/r/mbiLqmZWlihjLgz48HfYlMK6S5LVduw4MLEn7atXwtob66uLyRuXb8a48fOtWZkk67lSlpUxiVIDa4rDXtuUzrjQxPT2PfMWTx6/53Ov5X+LBjbfpmCctM2uf4+xRnThe1H1tVL+wmLotBAM6XJ9VNBFJuo7XZJ/4tbOEEvKo+SdqF/kpYHYc+TF8Gn7r7NuqA7ySJ7Ly8Yf7D9vevmuj7rduQkNH3u1+5Zg4lvv9G1yn395jWH4xNwKw6SVClBIZosXztsob7rd/MxX/+xxcQ0uF2ozxJofA5AZ1VP4Ma2Rn2/kxb48R9LtoIhwWwOLydYMpRzrthqOqfEPX+4nq9dCou4nAtcUxF7ObaIW+jllmVDTqmvQVGfTdhss5blezA5Vba2nwl7zaRFgAapEIofi6LQotCtcsrdFrXdLlet6kph18Q0HpqYbrvqlHbWMm2T5KxSu/Tz1JVqXTUOBnWu6RodAnebnCrjShfSFaMsGcrhliHzOkYATkUfBEiUVtZv8rapDnRWTc2FrHFKIlgFrpuzOjamNKDgwMVloFwseInXguoB+iOTZxL1altowVQvnRbZTWEXsMaPncOJPdvwO1/rTA/WvTNvWTYUGrAlDUZNTen9x9Llq9dQq7Vvd21exeqlaDqnxJ0RX+blnO7nkrboMjPr+jtvS/HcfWim9VpZcCkCktX4JuqzsZ3yly/JY/Z6veP101SjjEqb96+rDL5m1PsVdqzczI3GGdARLZCwymSuJ0V/GubYk40TTdx8fVOvJziOgfzFW7IIKvSPuOlZvvr8Gx0BXdJ0stq8wu5DM3hoYjr2jOJws4WByyNyAnzgZ1bhtR9UjYPxq9cb69NsRRyiXkNfcVyIACRrdaVwx56jxhN48AJD1hOWwUJKtuCp4OXwluNnH4dOtQbQWv+pK8r6e0q6pLKlWUfZ76nqYba8e5Wx6EZtXmVWHCdYmEnPstmC3wuVKh6ZPGOd8epmOrApjRG4ERykfT/0euVN+55tK1wUt1CMXlft30YTU2qkf822a9Ew12Pc9tnUlcqsPx0Q3fctbu/bMK4XhWzBlF/aapRh5+6wWcCwQNufGmtajgIg1Tb3O7dLI0R9Sv+olJsnKP0F1Q1sB1WSgVVtXrXKS+/fsQGlYgGCxqDf9uNoev8eP3neGFwEYzxdvOXEnm149cB9mA8JivS2hM3ClIoFfPkTG60DjbpS2HLgeNtnm2ZA5J8BdOXlBf92x3ud7z+vgBfOX8HY9nWtpuam+yxfEv/amn+tVLcG4mGfVzfoY3DXxDTW7jmKLQeOY98zZ62zxXG3Li9ifYz/WBrbvg4FL9/290ZV2LzzQFjQuOAxHDED4eUbqdMA2r6LlWqt43uoK3d2g39t0VeffyPz5xfA+F4E3+e48iJ4z08ux7deuWT9LmcVgM/PK6wc9tp+Vzffvsq6jm5FwQud6dSVMrNmm+31H1+u/LumJzuLBQ+CRuaE/wLC5dlaohlFf2AWpAuC3bHnaGudX8HLdzQAD57zbd9hPUujnzN4TtHCPpew7Y2rl4XdTO+JiS5Sc2LPNuu4YfehGWsg6iJs/8JSOsMCbf8YEEBrbKL3Iyx4vhkwoKOBFna1ZpCDOtcf3iB9ch0dKXX8mJnEmeHSFQNtQaItqNCzSK8euA9f/sRG637pH+KwAWvw5N2LGQX/Put1d3FeV1cIDTuBXanWrAGfjS6cMjlVjn28uAYFC5XG6Z95jmpNEEe9edXZRPe52nLgOHZNTGPpUK5j8O46WC0VCzi4cxOuzc239WwMGvZyuGXpEHZNTBsHSCb+fR72csgqo9C/pjTqcy8WvES/Ty/97ofw2M5NrWNdz7IkvXDg5Rvra//2+1d70pJBAXirNo+DOze11uKMPTljTP/1coKr18OLBa19ewE/fit9QSG/lcNe21psHbi4Hl9B/l2bVzf2K+y4TqJcqXYEV6YLjk+cPB86KI/6DgNwuggc9dOXVcAV9nuUNX2xt1gIv4gQ9to6YyJtNUrba+RFsGti2hpou7wvtiBtUKuiu2JRFBpoYcURsl7A3Ovca1MqpEuPMdfCEkC84hIC4ODOTZGl5KMWkfurdyalg0Tba9r668VlW0CfpGddWHEDvQbSVgI6rFG4Xv+lq4q5Fs0A3FpWJKGLcmSVipulYiCVEQgvte0fHO+amI58f/Rjoj6LYsHDtbn5vqhAqY8/l3Q8l2My6nWAzmMvqbCecd2SVeGnbhIAuUCK6CDwf+fiFlh5bOemyHOQa8GMqHOjS69YG/+5PZharbdZF/cJVt7MatwRXJes6XOA7bX8KbYmroVHXM6htvGD62+H7q3ob30xiMVSXIuiMKCjgRb1g5/VF7VfqmlGBUMrhz1MfcG9mWjcE2bU+xkn6E1TqdBfXc30mkD6AaOXE4x/vLOKqBZ1Ug4qFjxcvTbXkT7or7jp/3xNJ3Hb5+UPtqNOeLaqcHH6OIWJOzBKI2kFwBwANCu66SqqtvLwen9cviv+z2rtgKxrdK22CDSKI3zsfZ2Bb9zXy+qCS7eYgn7qnbC11GFsvwf+3yTXKtBh33cvJ4CgIwizXbyMOjcFK1eGXbxNM+6wbY+pPZLttcJ+1+JuW9KqpC6PM+1L1EW7fsWAjhaFqMGra6n+KP1WBndyqoyxp2baTiimUvwuzxMn8Mnq/QTStUdwed/9P/pxZiHDrk5GvZ5L83X/iWblcKMfVdQVSH3SCZshCgZqtlLpYSevtC0rTM+f9jltdJ8vl1mzKGEzk/qYjwpM9WcJIPUMdK/kBPjVu9dEzrb5W4d06/PsJ8WCh+tz9czTCmlh+M9brudy27lxZXNtXVTgaHu8rTWA/7HduFD9yOSZjqrJLrOhwdcKC+geC8ngieJy4c/1fGkTzEQYlCqXbFtAi4L+Iu4+ZM7pzioPvd9yr7MqZ6zv7xKIANnm9ZtSDF1S/sIqdvr5q4O5DEIFjYXwlYQzB/q1ogIMhWRVvMaPnQutIBcsUz46UsIjk2dalf/yInjgrvCKaWPb18UKkFx6Irk2/A17jXmgI3WsUq1h/Ni5yPTAuOvSgvQxHzWDeXm21qg0G7hy38/mFSJn24Ip3L38zRM03vfK7HVrpchhLxc78Gqku9ZRtTwuTaXQuLJKc+6VqN6Z/ch/3nKtAh12jrVVFb5QqUZm0djeOv/3Kuo7Fvc7ODlVNrbA8VfTtD1nublP+v1YviRv/C6mWcs7OVV2Ss2/PFvD2FPtbSPijGEuVKqZVg3tNwzoaODpL6cpONALrdNehUlbArkbsvph0s/jktfvEkjFeV0AkesEddBwebYWWqo6rDnr1WvRhQcUblx1TVLOWF8pdBnq+E+kQWEn1rCeWisKXqvkvf+99FfzfPp0GZtvX9Xxuv73bigHuI6Pw1JStbgXDYD2Qa6XF7w1Z96gsFL+uhVAmtky/zHvEpgO2kAXgHOgrY+RXu6hP606mJEA2AeXUSrVWqJCLFmvnfv/2bv3+DjK8274v2tXI2klWZKPwhY+gPERDHZQOIQk5WAggZC4kOJSSELbPORNm6c1IbSQ5gFCeIrf0gTap23aNM1DmpAUGohfCGnB5pADhAQ7NifZ5gxGBvkoW7ZW0h7u94+ZWc3OzmlnZ4/6fT8ffyztrnZnZ+/dnWvu+7ouLS5Y+/65ofIRq0Ggb3Mx41yLAels9YJWp151m9864Hmiy/5dYl3Oftb6x12fS1cJPSCtxxJ+J4+KOe4wK1K6bbP5feP1mNZ+ceMun8VZhaK/M83tu/GBFwK/r1IZlfvutC+99FPPbVmCYEBHDcHeo8Z6kBdFr5Fie7vVG6+19aUmfXv9rVNQahb3KKZ/jFtPnM1vHQid7+MVdDkptieeW+Dm9cXqdiBlVp4zDwrNanBeZ2StZ5Kt75egwVxvdyLwvnE6aeB1RrbJ0pfPb/bFLOVvv6fOhP71FnaG0L7s1m81QCMyl5eFKQIU1WMDhSd/zBNOYYI5U7GvoQC44vS5oRquN8cF4w6ztmvfry9jfWLH3roI6BT834921V616pTX5nWiy+m75Np7t2Hdvds8HyehxSES/ASJ3ch4Gl/Z8EIul9ftRFUxxx1BgiUzyPH6nDQrNbe3NHkG8/bvTKfCbk/s2FtQqKTYfWbOhFq31+/9XEwv3npYhumEOXTUcMqV79Yob3q7IAVfgjx3twDB6f6K5fWaXn/hEteDbLcz6kGXObnlDDrtj2JzudzGY9QVNO3MAipOMx5Blfp6btg64HuAVCy313Rqm4aLT56dO5AIUsDG/Dt7vgZQ3iIvtcSak1uNvDlrPo79/TYynq54URWz1UKY/eA1s9cbshiROd6rUeWz2sz3tN/MptNnrN/xQZix3msEK2GCfS/maxy2ymUxz8Xcp6U+B/P7JciKjKBL4u2KfS967bdaKXjnhTl0NGmVK9+tUddee+Vsec1+Acg74LLexmtmKAyvZYheZyC9Gg1bSxq7HSA6LdFw2x9e+W12bo1/rTMQo6lMoMDBfB5BzelO4K9+/ELoYM765RjmJIe5/6Lm9mwOjqRw/5YBxxMUfjlxTjP7YaqBXnXGvLyAsh5mYzpaJg4PqpErvM6YFYkLYB2q1SjIEhOUFER6zR54zcZ4MW8/2YI5ADgymsbDz3sHc24zMn7HB8WOdcHE7FbUzJf2mK7WUCeQi3kuB0dSuPfZXZ4nB4MsOS5m2WnY2cygnwFBAjO/4596woCOGk4t5rvVMr8vuCAfeEGWTZRyUOj2mpo5dcUKUtHMPCBwmh1w2h+jqYyeX+ITKJm5XV6Vuoo54B8ZTwc+GExo8ZLOJGtxyQvm/AJ9J15jRYsLOlqaIp99sY9X6xJQr5lCpy92t6VJbtXruhMabluzIu+yVbc+GulzLEdhDWtAW0wQa25LQou5Fh0pRi3Ul8kq56qGUamBp+jJq7JjNaSyyndbzLw4e2GoNpe8y6DFj5x4fYbYZ9nCsC77LGaWrtjnksoozxMEftuvxaWkZacmMxADisu9thIgLzfS7eRjrRW8K0Ws2htAFLXrL1yChBbPu6yR8t2i5hbompcH+cAL8uFXSkDt9pqG+YJ0q2h2+6Ur0NudgEAP+MwvlBsfeAEDRuuDgaGk64GEgv6F2N4ch1d6dlapgi/jMHkEJq8Dm4QWK3hOT+zY63ufWkwcn4OZkO62zdaCNW68xkp7cxNuvuTEgtdai0lJVdTcHnfNql60N8cdbp3/d2YxhONueBi3PPgSxHYI3qbF0Nrk/HUqoh9MWDk9x1KYB4xRM19Pp/eflfWRu9s03LV2Jaa1t/jef/RbHE5vdyK3jKuaamV/AHoQ9+b6i/Hm+oux9aYLcPMlJ3oWZao1T+zYi69seAHff+btvHy5o+MZxG3Pw178qBh+30Dm9RmlSjrgttcEsH+mAMj7nDpr/eM4Z+nMoj9nDiVTefmrxWhvbgpdJdrKzNn76kMv4VAyhe6EVvTnvwJy33XmyUfr97i5D/2Of+oJAzpqOG4H5/U2fV4pfgFwkA88vw+/UgNqt9c06EGY+V3gNRbWrOrFUzecizfWX4ynbjgXa1b1hgq0RsYzuHPtStdtc9pXYc4GBvl+G01lc8/JTEAP0hz7jt87xfV6M8Bxux/7c7EfZHR7HCwcSqawZlUvLju1N+/5pbPKtdx3UG5jVIt7fw2aS4jMg4GhZKqgMMRIKutaLMKc6bIegNnHcxSxWEapsgQEA0NJ3/eB9aUxn2+QmYFSXtLuhJbbfwkt/KGMFhOMjKdDLeWc2qaFPvi1i4vgAwunRXJfxdBiUhDgaHHJ9VQ0rVnVizt+7xR0J6J5vuU2MJTED37tvBLB2gJlapuWUHKGiwAAIABJREFU950Q5CRPWFHVh3E6ceYUtNy/ZQCXndpb1MmKroQW+oTToWQqskBoKJnCwZFU7jM3zOe/3yqjdfduw9GxNLS4e4BfT7jkkhpSo+a7lYNfT7sgFT69esqFadLttp1O9xFkvb65HcUWxQkTaCkgN6sRtDJqsUtjghZSML9cgxZasRai+OpDLznO/ikg1wvI6zGdHndgKOl5FrEroWHlVx8tWGITxZI0ty/oQz55OFEsIXJaumkdz259rQD3pZx25SrMABSft1buapgC4JaP5xescVvG6tc0HhJ+KeHhZBpXnD7XsaJssTJK4enXDnjeJkzPPSdi7JRiKxoX29uz2oK8b0Yd9qd+kqdyFV3DsH83uQUtT+zYi6duOFdv/fGfz/m2nDg8qr8XzJYvu40AMQhz7Lg1ZF8+ewqefu1AxZYYdxknH7y+x4eSKWgxwVSjB209F7xjQEdEngFwkCbmUTU6D7PdQLBS8mGCM7dAqzuhYXg07fqYZgNTINg+CVpavzuhYdvNFwDwP6Ayk/XNbfC777MW5ven89qdbnmC9oDV6XHdDkfN1gvlaMrdndBccyncitmYuY7XRlSN06+vlNv1cRFkfca2+VqbzzGKAKNWCYArz5hX8D66+ZITC95DMbiPt4QWR6sWKykvzCx5H9W+9rufKHIS3QpFlLPgRi1zOtnidZIHmGh47/We7UxEnwtsZZ8J82sMfscjOwP1DzT7yV126sT+CJr/NzKu93u1BoP21kOV/Fw6Op7Ghq0DvgXLUlmFtuYmbL3pggpuXfR8AzoR+Q6AjwHYo5Q6yeH6KwH8JfQxPgzg80qp54zr3jQuywBIBym7SUS1J8iMZ7VmRdes6g100B1mKYjbLNstH9eXI7m1KjAfq5h90tIUyz1OmxZDKqsKGrubj+u2bSb7QW+Qg6839+ffxu+gxomZd2g2OA/65R2XiebxQdlnXvT8HlXQ98q635xmDLWYFBSzEegH63c8srOo6qVevHLcvF7LIAdhChMH5LetWZHXyzFsVc2w5fTLya2VBFDYMFrgHsxFGaxXsjef00gwl5wGmbmzF4ooRZj3hRYr/n1eCQNDydxn1pzuhGvhFFNXQsPRsbTjdWa7D8C7WIqfsxZOc53Nclrp4dcYvJhxmkxl8k4KuQVz9v6K5nLr2y9dkVviv3soiVsefAmHR8MtmyxFKqNwy4Mvub5WVo1wgiLIwvO7AXzE4/o3APyOUmoFgK8B+Jbt+nOUUisZzBFRuZQrh88rH3PNql5ceca8grylYh/LDDKsB90KgrXvn+uZB2rdNmAiYOjtTuDOtSvzKisGCWbtX2jFBsBmbo01jyOorFJFJdObz9G6f9aeNhdN8fy8D/sBrNOMYSqr0N7clNuP1kBxYCiJI6OFORZhZJTKyyV0yqkLy54jY80H3XbzBbjKZZy6EaCknp2lenP9xbjL9vretXZl7gy60360N4z2Gn9mYaJqFT6IugDL1AAFaICJQhEbtg5g5VcfxYIbHsaCGx7GqlsfdSyyYWXPhR1zCRDE+NfbncBVZ8zLy7nraNWLHkX1/AV6S5AoWHPP/BrWDyVTjidJprZpud6Na1b1hs6v7E5oeHO/82eoU5VkwDkX3pRMZYoumhTk89tpNYWZm3btvdvy8o6jDOaKeSZDyVSgk2JddZIb6iVQY3ERWQDgJ04zdLbbTQXwolKq1/j9TQB9Sql9xWwUG4sTUTH8ys9bc8OiflxrWWWvGQQ3fo1uo9pOv7O0QVo5eJnapqGtuSnUrE4xjWLdlowF2Y9uTcHNBvJu99Gd0NDeEuy5ueW82WcUnZ6H2+P7tSUIMu6cyna7Fckx95lTPmMluL1fix2TbqyvZzlaPngxl9B6PWa3w6xqlNvplZfplPNczH4330tufxe2mbSbN9dfjAUeOaiV4vR5feW//gpP+eRFOrlr7UrX1R/2/Wt9T/vl0Ea97xuJObNai7lzQRuLR13l8o8B/JfldwXgURHZIiLXeP2hiFwjIptFZPPevf5ltYmITF5nQ3u7E2UL5uwza04J9n7C9sGxnzH3OsNun80LMqtoVpsMamgkFWrZivnYXmeYzSJ8XlVKg1Td9KrY6lW581AyhaduOLdg5qg7oRXM3gUJ5gDnSnVu+8DvQN6pkqadUxVXtwq35yydibPWP161BuhurS9Kae9hddRS1bLSuYYZn2CutzuBbTdfkBtngB4EKkTX1sBrwsKpLH4x+936HnMr1BFVi424CI674eGytOwoltNn3zOvHwx1X16zxwrIzayus8yCDQwlcc8zb7tW6OxOaGhxaa1SK0qpVlsqa0ueehVZURQROQd6QPdBy8UfVEoNiMgsABtFZIdS6udOf6+U+haM5Zp9fX2Nms9NRGXiVBShnOWHgzRcD8It98FrOViYpt7WfD63Jqt2QXrW2bc3yCyWVwXUMIV1NmwdcJ3BsO5Ht5zIc5bOzO0/J9b7sFf68wt6pnrkGtkPAs379WumK1JYtKbYsWeOAfMAO6NUrlLm/VsGHCvWltIcuRheBR5KIYBvflS1mZ9X5utoHa9mUFfuVyCZyuC6+57LbUfQEzX2z1u3vzNzHKOoDGr9v5rMk0LWz68w22WemAxaKMtKAY5ju5wFp6IURdGfUtR7Hl0kAZ2InAzg2wA+qpTab16ulBow/t8jIj8GcBoAx4COiKgUla60GXZmza6Y9gamUoPJoMVainku5yydib7500pqIxG2sM4dj+x0XZ5k3Y9uY8RrBsLttfCa0bNqa25yXYpqz9uwL+F143acGPT1sp8QyCiVe55O+8IM5r5++SmuS8GszEDd+lzatBhatHigghj2kxnmfimWWfDGLBgSVYGbYhUTvKy7d1uu7Ynba1EJGaVyJzm8qv22tzQVVDP0q8BrnjholGqsWkyw53Ayb9l/mJMPMQHGUpm8JaRB25a4adNiGE1nkXWIlZxODDnxOikF6DNr1Q7GolDveXQlB3QiMg/AAwA+pZR62XJ5O4CYUmrY+PkCALeW+nhENDkFmVWqZKXNMDNrTsIEolEFk36K6Y93/5YB9M2flitZ7fd31m0NOmMY5L6srNUfTU5jxKvaodMSTzMgCrptd65d6dgDyiyrvWZVbyT5YUHHntcJAa9ZlRsfeME3KDIDQ6/3ovl6O+WwCSaqDlrLnRe7X+IiOO24qfjt24dyl5UrmOtOaJ4zIMXOrA0MJQP1DHMSZXVSc0x4Vfu1zvoHORlhHR9986cFajlTjFIDoDDCvE52ZuBlr1Za6l0nU1nXcRd0t4+mMq7jV4uVf2btqjPm4d7f7IpkP3uxfh7XoyBtC34I4GwAM0TkHQA3A9AAQCn1zwBuAjAdwD+Jvo7ZbE/QA+DHxmVNAH6glPrvMjwHImpwYZYYlluYmTU3xQaiUQWTftyeo1P/LvPgz8zP8gtQ3Jqee722boGf2/4IWk3P6++dXpdic4rWrOp1bNRu5m2sWdVbcn6YOfacCiU8sWNv3j7zOiHgFcQnUxm0NMWgxcTx4Com+bmBfmNaoJ8VF6O5t73C6I0PvJC7z2KZjbpLPQQ0t8ntgNacpfKdVfW4DydhD17DLNXz4tZT85ylM3HHIztx7b3b9DL+AZb02Ss0Bm05U4zOVg1j6WzdFf8YS6uyBKJ++Zp+hVQA74Ct3BNzvd0J9M2fhnt+7b2NUbB+Htcj3wxEpdQVSqnZSilNKXWsUurflFL/bARzUEp9Vik11WhNkGtPoJR6XSl1ivHvRKXU/y73kyGixuQ1o1AtXi0Nys2tmEXU+YJuz9GtxYA1UDD/ttthGYt1W4O+tmbgZy0CYBZvKHV/FPv3YXKK/PZZsbOr3Qmt4HUBULCPvv/M2wX7rNulgFBXQvMsTgPoBWI6Wp3PBZsHpE6FNQDkyuWvs5U0H01lMbVNcywcU0q+W5jjY4G+xMzcr3euXYm71q5Eq0PBBi0muOXjJwZ+7cylxqVIaPFckSC7uEhBAaRSmUU4vnif/pqZwdz9WwbyXsMg+VlmuwirqE9CDSVTNV/8w0ml8wDNz6bb1qxwbGtSC6z5zZXaPfWcRxdZURQionKp1BLDYlWzmTpQmXxBp+fotqTSfnBm/q3Xksqgr61X4Gfm4oXdH3770779bssOnXKKzPvwm1UtZnmrfbmb6az1j/vOTHjNsh0d1xvw3n7pCtelcHO6E4Hed/acTq8Z22QqU9KMStTFWsx+d4C+3W7LH9eeNjc3uxrktTPzRksps3/7pSuw+a0DjrMqV5w+F8DE+y7KthPWYD1s7ptT8Bb1jCKAqlVnLUWlCg6Zj2U9+XjbmhXomz8t0JLZSuk2Zu79Zg+jVq3elFFgQEdENa9SSwzrSbWCSaD45aZe2xr0tfUL/ErdH25/77Qk1Cy2YZ2VcAuyTH77LOiBrVNlUFPQExyHkinHoNRccmQGyG7bGzSA2T2UzMuXi5K9955fIY6grONuw9YBzxwvswpskNcuihl0cwmw+Zx/+OtdyCiFuAiuOH0ublsz0Zzer1F4KcKEHW7P33oyJeoxUi+0uGDt++eWJXixf05pMUFHaxOutRTfMcfUmlW9WHXro0Xlmp61cFqoXnteEloMh5KpwOMsaHGXeEyQ8VjXWs6q2JVQf/PSRDTpVGqJIVBcf7fJyr6sKy6Sm5Epdn8FfW29esiVk9PMYCqr0N7c5Ljk0W3s+C3RDdIr8K61K3M5ik6C7os53QnfJaBe2+u3LNPUldByS0D9FNuDqq05/3x00G3yosUlN+7MQN5r1sRrX111xjzX19qtZ6Yfs1jMhq0DuG3NCrx2+0V4c/3FeO32iwqCOXuPTD/lWHIXpH8kMNEjsRaX/ZXb1DYNd3zyFNy2ZkXoceFJTSwh7k5ogJGval+2brr5khOLeh+9uT+JsxZOi2xztZh4FnJxohQCbbNXMDe1TatYykS5cIaOiGpepZYYBi3QUWpVxkbg1CcrTLGaoK+t0yyIQG+XUE5us16Hkilsuzl/aZ7fvvCbRbQuUbUuf7LPRrkpZqYoyLJZt+21v2ZORTESWhwSsKCJWWinmGp59v0bxUxPe3NT3v34bXuQfeXk4pNnh56NCfIeC1Ng5861KyNfcvf67Re7Xuf0GVrMsuNilFpWvzuh4ehYKtICIFpccMcnT8l7DW++5MTQ1U3dpLIKh5P6Uurh0XTBCYpkKoNbHnwp77W47NRePLFjb6DXYmAoGWnqQ5jnbuZpl7Js19xH9UxUDTRktOvr61ObN2+u9mYQ0STjtmzL2jPNKRcoocXr/uxeGEH2V5S+suGFgvydcux768FmzCW3xf4co9oXpY6vIFUu3aqQlrIvnQ7Qg/SsM4PVILd14rR/vZZf+pX17w247U4H5EFE0Z7C3E63cXXcDQ+H2pdR5nF1J7TcCQ+nMWlvXp/Q4rjs1N6Cy91occFpC6b6VjJ1q8oblAB4Y/3FJS3pdVsS6PQannjTf4cqBBRlw3nzcyDse7KSYqIvpYyqafpVZ8zLm+2uBSKyxSw46YUzdEREhiAFOkpt6t1ISilWE2aW84kdex0rIUa5752abts5LQmNqnBPpZrGRz3rXUzxHKAwFzDszJrT/nXLVzSDVa+D84GhJK69dxvamuOuB9btzXH8798NF/iW2p7C5DWuvBqBe83ARVmUYyiZwqpbH8XFJ8/OC9LMyqt2yVQGT+zYm+tjaQ3+zFxBq1RG4c393m02rMF5WOYsbNhZqIQWd3297fe5YetA6KquCv6vb1Dm501XRPdXTgqILJgD9CIsffOn1eV3OQM6IiJDkAIdtVpxsxrCFqsJ21ewEvve7YA7LoKsUq6BT1SFe0p9jsUEyuUurOMXWPndNgin/esXrPo9lgI8D6y725pD77eoxmpMxLUJstPz02ICqXCS2sGRVFEVMc2ed/bndI/L8lSvfSlAXvXbsLNr5ombYpaDWnsXeo3nLltLl1La8PR2JzAyHt2ywd1DSbQ1l5aT6qY7oWF4LO2Z0xZUORYZ1uvJWQZ0RESGINUbWXFzQtjm6mFnoSqx790OErNK4Y317jlBUTWaL/Y5WgM4ey5bmJxGv8coZiavmFnAMDN1golCIfb79QpWS50VNB/TaRmr33ONKk8so5Tra+uW4xh22WEpijne9ip85PWe8Hu/BF3+a9ed0PJOAgS9D2X73405i2nmx5YS7O8dHsV4hDNVfic1wrCezClX9dso1OvJWVa5JCIyBGkWXsmKm7UuyP5yEnYWqhL7Pmw1zbD7wu6cpTMdK1w6PUd7s3WnBs/JVAbr7t0WumKrV0P3IMwKhm+svzivQqdTNVnztnetXela8TBuTDNZc4aK3SbrdoVpwG0Gktb98ZUNLwTaT1FU4zSZJ0GcWPd7e0tTUcvS4iK5qohavDLTel7vY6d9ZhZECvKZsGZVb9HBnNmGxHofV5ahAffBkVRunHh9xvi9DlEGc1EyK2w6VfV96oZzcdUZ86q7gQ7q9eQsi6IQERWJVS5LU0oBkXLv+0oVvXF6HkBh7zcBcKVLon6xhRrCPI9yFL4Jso+9CuC4ndn3auzutS1uMy/dCQ1j6WygZaBuBUWc9lMxsxN+xS7Moh3W+7U/f68iKfb7t8+ilLPZtN8yZiuv8QD4zwIX+15xK45Rrn1ijh+31zsGADLR4L0WTG3TcPHJsx1zHE1mBUqvir0Lbng4sm0qtThM2GJH5cSiKEREZVLu3KNGV8ryxHLv+zDFQooNMt1yCPXS/fnBg8JEA2u7YpcGhSkgU468xSBLbm9bswJ986c57le3IhdDyVTuwDHoctM1q3qx+a0DjsGCOUNz4wPP+5a9dzugte4n+zjxIgi2PNO8H6+8VLf7MYuGOO3jqKpxCoATZrXj1T1HS6pO61UQyT7ze8cjO3Htvdvynk+xOZpu77k1q3pxxyM7Iw/ozPHjFoxkva6sMPtJpr7507DO4z1pdXAkhet/9ByAifelX+XZoO5auxJAaTmTa98/t26/2xnQERFRRVWqr2BYxQSNYQq8uAU0QavhmcLkYxUbiJUjbzFokOj2OgR93kEDWK/gEQCuu+8538dym6HzCrjcZhOss3peM0uCiaIdXkGy1wkUt30cVTVOBeCdg6O48ox5ud5mcZG85aL2x3c6QRJkzAR5LwY92Pd6n9RLjlWbFkNzUzzy4FMB+Mlz7+a1Q2n3qAprl8qovPel0/iMwQhii2AN7jdsHXANMr3cv2WgbqtcMoeOiIgqzi23qt54HUi7CRNUOXHKH9JigqltmuPtne7LKZfN7zFKzVsMm6docsozdBM04PUaj37l/BNaHFecPtdzPzmNE7MSotvfAPr+12LOz/bKM+blttMr4Ama32kdC1EWqzBbEphjydyfTnmGbjmb3S5juiuh5bb5uvue83wvWnM0/fIYvYoQxUKUC53apuGqM+blliBWwkgqWxDMuQyloiugDiVTea9RsQVUrOPVaXx+Y+1KnLVwWkn3GYbfZ3ct4wwdERFRSGGWJHr1CbPnbHkFT14znW55atb7CjKjUY7Z1FKW3G7YOoD7twzkzWwJ4Nk3btWtj2JoJBV6270ablv76XnN8rmNB2Xch9u+NX+25m055SH5zaT6zTpHtcTSze6hZKCltm63aWmKFfR002KCo+Pp3H5xe43slVCDzNads3RmwWXmPnJ6HC0mSLkkuHUnNGy9SW+yftuaFXkzkLEIm7kH4ZaDJwCa4tE16PZjD5jN18XcN+vu3VZ0ARrrbHgpQVm9zMDasSgKERFRSGGKhngVBQGiC578cvvKUfAkqm1z47bN3QkNh5Ip3zSjMIVhvrLhBcdm2G6FM5yUe1+XWsyn2KIhxTKDVqfXx1rYxa2AiwC4c+3KvDEzEqIVgxkMA/6N7+2vi9c+inkULPEqtFHuQLoY1qJClYoMrK9HKcVmrJ+fpe7PSnz+FYNFUYiIiDxEUTEzzGyT36xXVMtP/WZlKtGo3U3Y4jZu2xYkmAPCFYYxgzazmp85I3jPM2/nlhL63V9UfQoB73EbdjyX8zU3n6dbAGWdrfGaabSPmeNCVEc8OJLCF+/d5pufZe4P6772Gl9e1SdTGYVbHnzJ8bWxv26Q8jTLDuJQMoVtN+sziSu/+qhjcCWiB35R9TQ8OJLCF+/bhnistNnBy07V9+VZ6x/3DOYSWgzT2luweyhpFKEqHAlOs7P1gAEdERFNOmGKmTgJeyBdC5VSK9GoPWphmkzbhQlebluzIrdc7sYHXsgt7yymmiZQ+uyr37gNO6bc9qvXctNuWyN7J/bloX5BbdDA18xlC7NcMUixjZgIFtzwcMll8E1eFVityw3dKrhWgvV9f8vHT8T1//lc3jJSLSZYe9pc/OS5dyN93KwCsiUu9TSLmfi9t0dTWd+CQ24VTmsdAzoiIpp0guTzBFULwVkYUc4aVYrfNgdZblVKwFrKuIlinLg9vlmJM+z9u+1Xt30pALbdfIFvTz2lisvHDHIbr1y2qPi1ESiVfcyYz6laSVDWiqmA8+twztKZuH/LQNWWh2oxAQSOJxDM/elXAdf63q/mCoVyYEBHRESTTqN9mYdR6+0jnATZZjPAcJpdERQWyShGtceN2+NklAo1w2xy269+yyTNINWtObR92V6QoNbvNlG1VKg267LOa+/bVrWlloD+PrHvc/vr4LecsRzM97BZfAiAazuC3UNJ3Ll2petJHfvJqnpcoeCFAR0REU06jfZlHlY9zi56bbPb0j6TecwcdoltqeOm1LxNrxmIZCqDdfduy/Wei2KmefNbBxwLwlQzz6hRTrqYyzorxWv5aG+A8Vvp/e5UzRVwL2bTldAKKpiaS4Z7Hd5r9bhCwQv70BER0aRTjv5qVBuCzuCE6TlVyrhx67Fm7/1X7OPbhblfN275RPbL3XofevVEDKvWTrq0aTFMbdMgKOwr6CXIktG4CK46Y15ejza3fnZaTKDF87fA/K23O4E716507MEXdPxWcr9fdcY8bL3pAseTEm69GY+Op7Fh60Cu3+Cb6y/Ga7dfhDcdekuaJ1aSqQziRhM+t/6M9YIzdERE1HD8ZkLqcbkhBVPMTEKxsw6ljJso8jbN211333OeAUHYfFAgWGVH+367+ZITcf2PnsvLb9LikitJHyWnmZVy8iuMMpLKYiSVRbtHL0RAD86yShVVzOXrlzu3O7D3s5tjWZIYZGyGGb+V3O9mkRNrjqF1m5ubYkjZ9rVZTdTvudgLC2WUygW19fz5z4COiIgaStAKlvW43JD8+RVGsN+2WFG3XAgbVPodXIdZIhe0L5pTY2jAv5hJFCdQ7I/VldAgglzz+KGRcc/AqhjW3C2//eL3mGZwFrTdgsB/OfDRsXRutverD72Emy850beHWtjxa1/OGFUFUCfWExJOn+duhpIpLLjhYccllqYoC2LVEgZ0RETUUBr1C5uCcZpJcKqQV+kltlHmbdoPrt0er1hBlqu67TevQCGqNiFBHitMfzo7t6bsXvvbz+a3DuCOR3YGDoK8brdh60BBW4GDIymsu3cb1t27zTOgKYW1xUKY2boYgrWNACZOSIQpguM1vqpd2KhcmENHREQNpVG/sCmYNat6cfulK/Lyju74vVNwxydPybus0vkyUedtmrlCpeRF2Xm9R0rZb14nWaJWaq5XXMTxOZr7O0gBESf3PPN2UcGg1+Pc8cjOvGDOLso8SrfHD7P0Mh4vzH1zyzs0X8ewn9tu48ttfNRabmaxOENHREQNhRUsyW0Gp5oztG7LEgG9JHyQpYheyxajWM7o9t7p7U74LuXzUsmTLEFnaN1mbf0C1usvXOJaOt9LMcsT/QLyIPvNGtBEnSvs9fhT2zQcHEkVXB4Xcewh15XQMJbOulabdBuTIvBt9eC0nY1W3dLEGToiImoorGBJtcqc5XnDqLwHIHDlS68qmfb7LaXBeDneO5WcFQk6Q2u9DNADDjMI8prZWrOqF21a+Q6fp7ZpvkFl0P1mjpGg4+us9Y/juBsexlnrH/fcB26P39udwM2XnOg4htwKwRxKpgpeL+vzdxuTV54+z7fiq9N2Oo2Peq5uaRJVzU6GLvr6+tTmzZurvRlERFSnoirAQMFxnxfvrPWPB54RK+a2pSjH6+iUcxVkNqwSnLZNAFx5xjzctmaF69/Yc9hM3QkNR8fTebNRQQqIuPVdK/bxrWICON3EPmaKfX38bu80htzyD4OMX7cxaV7uVKSlVsZXqURki1Kqz/d2DOiIiIioFLV8wF7LjrvhYccDfQHwxvqLQ9+2FtVqwO8WKAuAO9euDLX81X7dOUtn4v4tAwVBowJCFzDZsHUAtzz4EoaShcsb/djHjNs+gMf2BXk9rbfpcgh0o/yMqNXxVSoGdERERFQRlZo9ajS1OEM32bgFykC0+7acAYfXc3Bif15+fx8m8HI6yaPFBB2tTbkWE9UIuuot8Asa0LEoChEREZWElUXDKaZAQ6MWc6g2r76FUY7fcva9LKb3ohaXgjHj9/dh2r44VcJMZRXampuw9aYLAt9PlKJun1FLAmV1ish3RGSPiLzocr2IyN+LyKsi8ryIvM9y3WdE5BXj32ei2nAiIiKqDY1aCrzciinQ0KjFHKrt+guX+JbOr3VOhUPctDc3FYyZIH9fbHBbiyd5Ktk+o9KCztDdDeAfAPy7y/UfBbDI+Hc6gG8COF1EpgG4GUAf9KXCW0TkQaXUwVI2moiIiGoHZ4/CK2bmppyzPJPVmlW92PzWAdzzzNsFRTWKGb/VXMoXpNG8ySnnrhyN6muxfUwtBplRCTRDp5T6OYADHjf5BIB/V7pnAHSLyGwAFwLYqJQ6YARxGwF8pNSNJiIiotrB2SOqZ7etWYE7164MPX69WkpUitm6wm220SSA43ZF3ai+2u1jnNowNPJKgqhy6HoB7LL8/o5xmdvlBUTkGgDXAMC8efOHUbNsAAAgAElEQVQi2iwiIiKqBM4eUT0rZfx6LeWr9HvCLx9OAZ7bFVWj+igb3hfLLVfuslN7C6qNNspKgpopiqKU+haAbwF6lcsqbw4RERERka9aWsrntPzZzm+7ojo5U62TPG4B9hM79uL2S1fUVZXLoKIK6AYAzLX8fqxx2QCAs22XPxnRYxIRERERVVUt5YuVIx+u3ngF2I26kiBQDl0ADwL4tFHt8gwAh5RS7wJ4BMAFIjJVRKYCuMC4jIiIiIio7lU7X8wu6ny4etPIuXJuAs3QicgPoc+0zRCRd6BXrtQAQCn1zwB+CuAiAK8CGAHwh8Z1B0TkawCeNe7qVqWUV3EVIiIiIqK6Uc18sXrcrnKbjFV3RanaS1fr6+tTmzdvrvZmEBERERFRnalmG4koicgWpVSf3+1qpigKERERERFVVqMEP1aNmivnhgEdEREREdEk5FbiH8CkCojqXVRFUYiIiIiIqI549dCj+sGAjoiIiIhoEqqlHnoUHgM6IiIiIqJJaDKW+G9EDOiIiIiIiCahWuuhR+GwKAoRERER0SQ0WXvVNRoGdEREREREk9RkK/HfiLjkkoiIiIiIqE4xoCMiIiIiIqpTDOiIiIiIiIjqFAM6IiIiIiKiOsWAjoiIiIiIqE4xoCMiIiIiIqpTDOiIiIiIiIjqFAM6IiIiIiKiOiVKqWpvQwER2QvgrWpvh4MZAPZVeyNoUuBYo0rhWKNK4nijSuFYo0op51ibr5Sa6XejmgzoapWIbFZK9VV7O6jxcaxRpXCsUSVxvFGlcKxRpdTCWOOSSyIiIiIiojrFgI6IiIiIiKhOMaArzreqvQE0aXCsUaVwrFElcbxRpXCsUaVUfawxh46IiIiIiKhOcYaOiIiIiIioTjGgIyIiIiIiqlMM6AIQkY+IyE4ReVVEbqj29lD9E5E3ReQFEdkmIpuNy6aJyEYRecX4f6pxuYjI3xvj73kReV91t55qnYh8R0T2iMiLlsuKHl8i8hnj9q+IyGeq8VyotrmMtVtEZMD4fNsmIhdZrrvRGGs7ReRCy+X8niVPIjJXRJ4QkX4ReUlE/ty4nJ9tFDmP8VaTn2/MofMhInEALwM4H8A7AJ4FcIVSqr+qG0Z1TUTeBNCnlNpnuexvABxQSq033vBTlVJ/aXxY/E8AFwE4HcDfKaVOr8Z2U30QkQ8DOALg35VSJxmXFTW+RGQagM0A+gAoAFsAnKqUOliFp0Q1ymWs3QLgiFLqb223XQ7ghwBOAzAHwCYAi42r+T1LnkRkNoDZSqnfisgU6J9JawBcDX62UcQ8xtvlqMHPN87Q+TsNwKtKqdeVUuMA/gPAJ6q8TdSYPgHgu8bP34X+wWFe/u9K9wyAbuODhsiRUurnAA7YLi52fF0IYKNS6oBxoLMRwEfKv/VUT1zGmptPAPgPpdSYUuoNAK9C/47l9yz5Ukq9q5T6rfHzMIDtAHrBzzYqA4/x5qaqn28M6Pz1Athl+f0deL+gREEoAI+KyBYRuca4rEcp9a7x83sAeoyfOQYpCsWOL447KsUXjGVu3zGXwIFjjSIiIgsArALwa/CzjcrMNt6AGvx8Y0BHVB0fVEq9D8BHAfypsWwpR+lrobkemsqC44vK7JsAFgJYCeBdAF+v7uZQIxGRDgD3A1inlDpsvY6fbRQ1h/FWk59vDOj8DQCYa/n9WOMyotCUUgPG/3sA/Bj6lPyguZTS+H+PcXOOQYpCseOL445CUUoNKqUySqksgH+F/vkGcKxRiUREg35wfY9S6gHjYn62UVk4jbda/XxjQOfvWQCLROQ4EWkG8PsAHqzyNlEdE5F2I8EWItIO4AIAL0IfV2a1rc8A+P+Mnx8E8GmjYtcZAA5ZlpcQBVXs+HoEwAUiMtVYUnKBcRmRJ1uO7+9C/3wD9LH2+yLSIiLHAVgE4Dfg9ywFICIC4N8AbFdKfcNyFT/bKHJu461WP9+aor7DRqOUSovIF6C/2eMAvqOUeqnKm0X1rQfAj/XPCjQB+IFS6r9F5FkA94nIHwN4C3olJQD4KfQqXa8CGAHwh5XfZKonIvJDAGcDmCEi7wC4GcB6FDG+lFIHRORr0L+MAOBWpVTQ4hc0SbiMtbNFZCX0pW9vAvgcACilXhKR+wD0A0gD+FOlVMa4H37Pkp+zAHwKwAsiss247MvgZxuVh9t4u6IWP9/YtoCIiIiIiKhOccklERERERFRnWJAR0REREREVKcY0BEREREREdUpBnRERERERER1igEdERERERFRnWJAR0REdU9Ejhj/LxCRP4j4vr9s+/3pKO+fiIioFAzoiIiokSwAUFRAJyJ+PVnzAjql1AeK3CYiIqKyYUBHRESNZD2AD4nINhG5VkTiInKHiDwrIs+LyOcAQETOFpFfiMiD0BvBQkQ2iMgWEXlJRK4xLlsPIGHc3z3GZeZsoBj3/aKIvCAiay33/aSI/EhEdojIPSIiVdgXREQ0CfidlSQiIqonNwD4klLqYwBgBGaHlFLvF5EWAE+JyKPGbd8H4CSl1BvG73+klDogIgkAz4rI/UqpG0TkC0qplQ6PdSmAlQBOATDD+JufG9etAnAigN0AngJwFoBfRv90iYhosuMMHRERNbILAHxaRLYB+DWA6QAWGdf9xhLMAcCfichzAJ4BMNdyOzcfBPBDpVRGKTUI4GcA3m+573eUUlkA26AvBSUiIoocZ+iIiKiRCYD/qZR6JO9CkbMBHLX9vhrAmUqpERF5EkBrCY87Zvk5A37fEhFRmXCGjoiIGskwgCmW3x8B8HkR0QBARBaLSLvD33UBOGgEc0sBnGG5LmX+vc0vAKw18vRmAvgwgN9E8iyIiIgC4hlDIiJqJM8DyBhLJ+8G8HfQlzv+1ihMshfAGoe/+28A/4+IbAewE/qyS9O3ADwvIr9VSl1pufzHAM4E8BwABeAvlFLvGQEhERFRRYhSqtrbQERERERERCFwySUREREREVGdYkBHRERERERUpxjQERERERER1SkGdERERERERHWKAR0REREREVGdYkBHRERERERUpxjQERERERER1SkGdERERERERHWKAR0REREREVGdYkBHRERERERUpxjQERERERER1SkGdERERERERHWKAR0REREREVGdYkBHRERERERUpxjQERFR3RGRJ0XkoIi0VHtbiIiIqokBHRER1RURWQDgQwAUgI9X8HGbKvVYREREQTGgIyKievNpAM8AuBvAZ8wLRSQhIl8XkbdE5JCI/FJEEsZ1HxSRp0VkSER2icjVxuVPishnLfdxtYj80vK7EpE/FZFXALxiXPZ3xn0cFpEtIvIhy+3jIvJlEXlNRIaN6+eKyD+KyNetT0JEHhSRa8uxg4iIaPJgQEdERPXm0wDuMf5dKCI9xuV/C+BUAB8AMA3AXwDIish8AP8F4P8AmAlgJYBtRTzeGgCnA1hu/P6scR/TAPwAwH+KSKtx3RcBXAHgIgCdAP4IwAiA7wK4QkRiACAiMwCsNv6eiIgoNAZ0RERUN0TkgwDmA7hPKbUFwGsA/sAIlP4IwJ8rpQaUUhml1NNKqTEAfwBgk1Lqh0qplFJqv1KqmIDudqXUAaVUEgCUUt837iOtlPo6gBYAS4zbfhbAV5RSO5XuOeO2vwFwCMB5xu1+H8CTSqnBEncJERFNcgzoiIionnwGwKNKqX3G7z8wLpsBoBV6gGc31+XyoHZZfxGRL4nIdmNZ5xCALuPx/R7ruwCuMn6+CsD3StgmIiIiAAATvImIqC4Y+XCXA4iLyHvGxS0AugHMBjAKYCGA52x/ugvAaS53exRAm+X3Yxxuoyzb8CHoSznPA/CSUiorIgcBiOWxFgJ40eF+vg/gRRE5BcAyABtctomIiCgwztAREVG9WAMgAz2XbaXxbxmAX0DPq/sOgG+IyByjOMmZRluDewCsFpHLRaRJRKaLyErjPrcBuFRE2kTkBAB/7LMNUwCkAewF0CQiN0HPlTN9G8DXRGSR6E4WkekAoJR6B3r+3fcA3G8u4SQiIioFAzoiIqoXnwHwf5VSbyul3jP/AfgHAFcCuAHAC9CDpgMA/l8AMaXU29CLlFxnXL4NwCnGfd4JYBzAIPQlkff4bMMjAP4bwMsA3oI+K2hdkvkNAPcBeBTAYQD/BiBhuf67AFaAyy2JiCgiopTyvxURERGVTEQ+DH3p5XzFL2AiIooAZ+iIiIgqQEQ0AH8O4NsM5oiIKCoM6IiIiMpMRJYBGIJevOWuKm8OERE1EC65JCIiIiIiqlMlzdCJyEdEZKeIvCoiN7jc5nIR6ReRl0TkB6U8HhEREREREU0IPUMnInHoVb7OB2CWYr5CKdVvuc0i6NW+zlVKHRSRWUqpPX73PWPGDLVgwYJQ20VERERERFTvtmzZsk8pNdPvdqU0Fj8NwKtKqdcBQET+A8AnAPRbbvM/APyjUuogAAQJ5gBgwYIF2Lx5cwmbRkREREREVL9E5K0gtytlyWUv8nvvvGNcZrUYwGIReUpEnhGRj7jdmYhcIyKbRWTz3r17S9gsIiIiIiKiyaHcVS6bACwCcDaAKwD8q4h0O91QKfUtpVSfUqpv5kzfmUUiIiIiIqJJr5SAbgDAXMvvxxqXWb0D4EGlVEop9Qb0nLtFJTwmERERERERGUoJ6J4FsEhEjhORZgC/D+BB2202QJ+dg4jMgL4E8/USHpOIiIiIiIgMoQM6pVQawBcAPAJgO4D7lFIvicitIvJx42aPANgvIv0AngBwvVJqf6kbTURERERERDXaWLyvr0+xyiUREREREU1WIrJFKdXnd7tyF0UhIiIiIiKiMmFAR0REREREVKcY0BEREREREdWppmpvABERERERUaVt2DqAOx7Zid1DSczpTuD6C5dgzaream9W0RjQERERERHRpLJh6wBufOAFJFMZAMDAUBI3PvACANRdUMeAjoiIiIiIGkZyPIN9R8aw/+g49h8Zw/4j49h3VP9/v3H5r17bj3Q2v9p/MpXBHY/sZEBHREREREQUlVQmi4NHx7HvyDj2G4FZYcA2jgPGdSPjGcf7aWuOY3pHM6a3txQEc6bdQ8lyPpWyYEBHREREREQVo5TC4WQ6b9ZsnyU42390TA/ejKBtaCTleD9NMckFaNM7mnH8jHZMb2/G9I4W43Lj5/ZmTO9oRlvzROhz1vrHMeAQvM3pTpTteZcLAzoiIiIiIipJkGWO+y0zbG4zZN1tWi4QW3LMlFywNr2jBTMswdqM9hZ0JpogIqG29/oLl+Tl0AFAQovj+guXhLq/amJAR0REREREedKZLA6MGEGYfdbM+nsRyxzndLdiRW9XLkAzZ86mt7dgRkczprY3Q4tXpquamSfHKpdERERERFTzgixz1P/XLzvoscxxmjFTNqOjGQumt+XNmtmDNesyx1qzZlVvXQZwdrW7h4mIiIiIyNVoyljmmDeDNrHE0XrdgaPjSGX8lzku7unA9OOn5wdmlgCus1VDLBZumSOVBwM6IiIiIqIa4LXM8YCtyuP+I2M46rLMsVWLYUZHC6Z3tGB2VytO6u3MBWczcgVDKr/MkcqDAR0RERERURkopXB4NG0pCmKZRXMov++2zDEek7xZsvnT8pc5TjOWN5rBWi0vc6To8dUmIiIiIgrIbZmj2QPNnpfmtsyxK6HlArJFszpwxvHTcrNmEzloXOZI/hjQEREREdGklc5kcXAklT9jljeDVvwyx2M6W3HinM6CSo7mLNrUtmY0N3GZI0WDAR0RERER1ZwNWwdClZR3W+Z44KhTZcdxHBwZh3KYRIub1RyNvLN589osQVl+gMZljlRNHHlEREREVFM2bB3Ia/o8MJTEX97/PN4+cBQrersdGlgXv8zxhFkdON1SHGSaLVjrSnCZI9UHBnREREREVHaZrMLwaAqHkikMjej/W/8dtlz++M49GE9n8/5+LJ3FNza+kndZS5O+zHFGRzN6OluxfHZnLu+MyxxpsmBAR0RERESBZLMKw6PpgmDsUDKFoeR4LjDLXWYJ3IZH05733dwUQ3dCQ1dCKwjmrB74kw/kGli3Ncchwlk0mtwY0BERERFNIkopDI+lcWjEMjPmEKAdcppFG0055puZmuMxdCY0dCWa0JXQ0NPZisU9U9CV0IzLtVzQ1tVm/G/8a9Xiufs5a/3jGBhKFtx/b3cC75s3tRy7hahuMaAjIiIiqjNKKRwdzxizYN4zY9brhoyfsx5BWVNM8gKu6R3NOH5me17wlfevTUN3otkIymKRzJhdf+GSvBw6AEhocVx/4ZKS75uo0TCgIyIiIqoCpRRGjKAsyOzYkCUwO5xMIe0RlcXNoMycGWtrxrzp7ROzY9brEhq6LbNltbCM0axmGabKJdFkw4COiIiIKCSlFEZT2fzAy5gxs8+MOc2YuVVjBICYIBdwmf/mTk0UzJB1t2kFt+toaap6UFaqNat6GcARBcCAjoiIiCa90VQmP5fMYYasYMZsRA/MxjPuBTxEgCktTXnLEud0JfICsO4252WMHc1NLJtPRL4Y0BEREVFDGEtnCnLJ3PLJ7JeNeVRVBCaCMjPgWjSrIy/4Kpg1M4K3jtYmxBmUEVEZMaAjIiKiUDZsHYg8xymVyV++6DZTZs6OWS+zFtBw0tHSZMkba8LxM9v1wKstP58srxJjQsOU1iY0xdm/jIhqEwM6IiIiKtqGrQN5VQgHhpK48YEXAAAfO3k2Do+m83LJ8vLJPJYzjox7B2VtzfG8mbD509sKlio6VWLsTGjQGJQRUQMS5dVMpEr6+vrU5s2bq70ZREREk9JYOoPh0bTxL4XDSf3/4dE0Do+mcHg0jX/75es4OlYYfAkAvyOLVi2WtyzRK5/MWoWxs1VDcxODMiKaHERki1Kqz+92nKEjIiJqIOlMFkfG0hPBly0YM4M06++HR9MYTuqB2vCofz6ZFwVg3epFnpUYW5rivvdDRETBMKAjIiKqEdmswtHxdC6wss+QHR5NFwRmh5OpvNm0oz5LFgF9hmxKq4bO1qbc/8d2J9CZ0H+f0tKEKa1N6Exo+u+tTei0/N/R2oQP/80TGBhKFtx3b3cC61YvLsfuISIiBwzoiIiIIqCUQjKVmQi0RtMFwdZEIDZx3WHLbNmRsTT8MiG0uOSCKzPYmjWlNfd7LihrbUJnLhAzb69fF8WyxesvXJKXQwcACS2O6y9cUvJ9ExFRcAzoiIiIkJ83NhGI5eeN2fPJhsfyf09nvaOxmKBgxuvYqW164JXID7o6LUGYeV1nq4aWplhNNIw2q1lGXeWSiIiKw4COiIjqnpk3djhpW45oDcqSE0FY/gxZ8Lyxjpam3DLFKa1NmNnRgoUzO5yDMNts2ZRWDe3N8ZoIxqKyZlUvAzgiql/P3wc8ditw6B2g61jgvJuAky+v9lYVjQEdERFVVTarcGQ8nT8jlswPygqXKuYvV/QrdQ/oywGtM15dbc04dlqbZVliU8HsmXUJY0cLG0QTETWM5+8DHvozIGXkAh/apf8O1F1Qx4COiKiBlaPxs5U9b+yQV0VFSz5ZsXljzfFY3tJDM28sP1/MEoTl3Vb/nz3IiIgmsfQYMHYEGB8GxoaBR/5qIpgzpZL6jB0DOiIiqgVejZ/NoM7MGysMtiaWIzrlk1l/D5I3lguuWvQZr7nT2iaWJbbmL0t0CtJaNZa5JyKaVJQC0qN6EDZ2GBg/Yvw8bPw8bPnZepthI3AzLjN/zowHe9xD75T3eZUBAzoiogb1N4/syKtACADJVAZf+s/ncNvD/Tg8msZ4gLyx/BL2TejpbMUJs5yXJTpVVmxrsLwxIiJyoRSQGrEEXsMeQdhw/oxZLgiz3CabDva4zVOAlg6gZQrQ3KH/3L5g4ufc5VMmfn74i8DRvYX31XVspLukEhjQERE1kNFUBr98ZR82bR/E7qFRx9ukswrnLz+msLJii5b3e2dCQ0dzE2LMGyMialzZLJA6GkEQZlyu/E8UQmLOQdiUHofLp9h+7jBuY/ystQOxEEvq06P5OXQAoCX0wih1hgEdEVGd2zs8hid27MHG7YP4xSt7MZrKYkpLExJaDMlU4Rdrb3cCt1+6ogpbSkREkchmHJYgHnae5XJagmhdqjh+BIBPIjMASHwiuDIDrNZOoKvXOThr6fQIwtqAaq/cMPPkWOWSiIgqTSmF1/Yewcb+PdjY/x627hqCUnqgtrZvLlYv78Hpx03HT194l42fiYhqRSbtMLNlD8Jss19u+WKpo8EeM6ZNBFFmgNU2DZg632X2yz4TZvxdSwfQ1Fr9ICxqJ19elwGcHQM6IqI6kM5kseWtg9i0fRAb+wfx5v4RAMCK3i6sO28xVi+fheWzO/Ny1dj4mYjqWi30CEuPB5v9ClK0I530fzwAiLdYgrAp+sxWxyxg+sJgSxDzgrCW8u4fqgkM6IiIatSRsTR+8fJebOwfxOM792BoJIXmeAxnLpyOP/7Q8Vi9bBZmdyU874ONn4moLoXtEaaUXp7eLQjLq4rolC9mq5YYtDKi1mYrwDEF6JwTfAmiNVCLa6XtO5p0GNAREdWQ9w6N5mbhfvXafoxnsuhu03DukllYvbwHH148Ex0t/OgmogaWzQKbbnbuEfaTa4E3fuYwE2ZZqhi4MmJHYRXE7rm2IGyK++xXs+Xv4vxcpurh6CMiqiKlFLa/O4yN/YPYtH0QLwwcAgDMn96GT585H6uX96Bv/lQ0sSk2ETWCTAoYfg84vBs4PAAMvzvx82Hj5+F3gWzK+e/HjwCvPp4fhLXPDBGEtQMx9rekxsCAjoiowsbTWfz6jf3Y1D+ITdv3YGAoCRFg1dxu/MVHluCC5T1YOLODvduIqL6MjxgB2oARpFn+DRv/H9mDgoqKTQl9eWLnHGD+B4DO2cDm/wuMDhU+Rtdc4NoXK/J0iOoFAzoiogo4NJLCky/vwcb+Qfxs514Mj6XRqsXwwRNm4s/PW4Rzls7CzClMXieiGqQUMHqoMDizzqodHnAOwFq7gM5ePVjrOWniZ+u/1u7C6omzljdMjzCicispoBORjwD4OwBxAN9WSq23XX81gDsADBgX/YNS6tulPCYRUb3YdWAkt5TyN28cQDqrMKOjGRetmI3zl/fgrBNmINHMJT9EVEXZLDCyz3tW7fBuIDVS+Lfts/SAbOoCYP6ZRoDWC0yZbQRus/WljWE0UI8wonILHdCJSBzAPwI4H8A7AJ4VkQeVUv22m96rlPpCCdtIRFQXslmF5wcOGUspB7HjvWEAwKJZHfgfHz4e5y/vwcpjuxGLcSklEVWANV/NGpz55avFmoygzJhVW3ShEazNnphh6zgGaGou7/Y3SI8wonIrZYbuNACvKqVeBwAR+Q8AnwBgD+iIiBrWaCqDp1/bh439e/DY9kHsGR5DTID3L5iGr1y8DKuX9WDBjJBnqImI3OTlq1ny1qw5bL75avZZNePn9plAjIWYiOpFKQFdL4Bdlt/fAXC6w+0uE5EPA3gZwLVKqV0Ot6kLZ599dsFll19+Of7kT/4EIyMjuOiiiwquv/rqq3H11Vdj3759+OQnP1lw/ec//3msXbsWu3btwqc+9amC66+77jpccskl2LlzJz73uc8VXP+Vr3wFq1evxrZt27Bu3bqC6//6r/8aH/jAB/D000/jy1/+csH1d911F1auXIlNmzbhtttuK7j+X/7lX7BkyRI89NBD+PrXv15w/fe+9z3MnTsX9957L775zW8WXP+jH/0IM2bMwN13342777674Pqf/vSnaGtrwz/90z/hvvvuK7j+ySefBAD87d/+LX7yk5/kXZdIJPBf//VfAICvfe1reOyxx/Kunz59Ou6//34AwI033ohf/epXedcfe+yx+P73vw8AWLduHbZt25Z3/eLFi/Gtb30LAHDNNdfg5Zdfzrt+5cqVuOuuuwAAV111Fd555528688880zcfvvtAIDLLrsM+/fvz7v+vPPOw//6X/8LAPDRj34UyWR+eeaPfexj+NKXvgSAY6/Wxl5Tcws+99ffxqbtg3jg3/4ew69vRTwm6E40Y2q7huN6j8G9t/8YAMcex97dBdfzc49jz3PsLV6Mh+7/D3z9G3cCmTG9D1p6DMiM4XufPQVztSHc+8uX8c2nC/PVfvSpHsyYPQ93bxvH3U8fBZqmAfFmvbl0vBk/ffDHaJvei3/65jdx3933QT80m3iNOfYm+djj517dKndRlIcA/FApNSYinwPwXQDnOt1QRK4BcA0AzJs3r8ybRURUnGQqg4NHx3FwJIWjmRhe/dHzmN3VihPndGIo2YnO1ibEjKT+5iae2SYiB9msPmu271Vg5IAesKUtQdv3LgUSB4EXh4DdtobWcQ0Y2Q8sOB6YOw2Yuh2It+jLHs3///zHwIwZwN13Ay/fXfj4bdMKi48QUd0TpZT/rZz+UORMALcopS40fr8RAJRSt7vcPg7ggFKqy++++/r61ObNm0NtFxFRFDJZhd++fVAvatI/iNf3HQUAnDinE6uX9eD85T04cU4nWwsQkc41X81aZMQjX8265LFzdv5SyCmzy5+vRkQ1R0S2KKX6/G5XygzdswAWichx0KtY/j6AP7BtxGyl1LvGrx8HsL2ExyMiKqujY2n84pV92Ng/iCd27sGBo+PQ4oIzjp+Oq89agPOW9aC3O1HtzSSiSsvlq1mKitgbYh8ZRKB8tSm2kv3tM9ngmohKEjqgU0qlReQLAB6B3rbgO0qpl0TkVgCblVIPAvgzEfk4gDSAAwCujmCbiYgiM3h4FJu267NwT722H+PpLDpbm3Du0llYvbwHH148E52tWrU3k4jKweyv5tYM25xtSx4s/NvWrongrOckS5BmKTCSmMoljkRUdqGXXJYTl1wSUbkopbDjveFca4Hn3jkEAJg7LYHzlx2D85f3oG/BVGhx5sER1bVcfzWPWbXDu4HU0cK/bZ9pWf44x9JXbc7E7y0dlbYEja8AACAASURBVH9ORDSpVGLJJRFRXUhlsvjNGwdyTb7fOahXN1s5txvXX7gE5y/vwaJZHcyHI6oXZr6a28za8G49YLPnq0nc0l/tRGDR+ZYgzRKsMV+NiOoIAzoiakiHkin87OW92GTkww2PptHSFMMHT5iBPz3nBJy3dBZmdbZWezOJyC6VdAjObP8c89VaJ2bV5p6RP8NmNsRmvhoRNSAGdETUMHYdGMFj2wexafsePPP6fqSzCtPbm/GRE/WllB9cNANtzfzYI4rM8/cBj90KHHoH6DoWOO8m4OTLnW/rmK/m0BDbKV+tpWtiJq1nuWUppKW4CPPViGiS4pENEdWtbFbhxd2HsKl/EI/2D2LHe8MAgIUz2/HZDx2P85fPwsq5UxGP8SCPKHLP3wc89Gf6jBoAHNoFPPgFYPc2YPrx+blrQfLVuucBc0+3le7vZb4aEZEPBnREVFdGUxn86vX9uaImg4fHEBOgb/40/NVFy3Deslk4fiYP/oiKlkkDY4eB0SF9Ji1p/J/3z3LZ60/qDbGt0mPAM/+o/5yXr7Z8Il/NWmBkyjFAU0vFnyoRUSNhQEdENe/A0XE8sWMPNvYP4uev7MXIeAZtzXH8zuKZWL2sB+csnYVp7SxiQJNcNguMD7sEYw7Bmf0248Pe9y8xvVR/a7f+vz2Ym7ghcN0O5qsREVUIAzoiqklv7DuKjf3vYVP/Hmx+6wCyCujpbMHvrurF6uU9OPP46WjVeLBIDUQpIDXiH4wlhxwCtCFg9DAKCoXYtXQZQZnxb9px+b+bwZr5L2H5vbkjP0ftzpP0ZZZ2XcfqM29ERFQRDOiIqCZksgrbdh3Eo/16k+/X9uq5NkuPmYIvnHMCVi/vwUlzuhBjPhzVstSoSzAWcBljNu19/1p7fqDVOQeYtawwELMHY61dQEtntDNm592Un0MHAFpCv5yIiCqGAR0RVc3IeBq/eGUfNvUP4vEde7D/6DiaYoIzjp+OT50xH+ct68HcaW3V3kyaTDIpfaZr1GEWLMgyxvSo9/03teYHWW3TgGnHewdird3Gv04grlVmPwRhVrMMWuWSiIjKggEdEVXUnsOjeGzHHmzqH8QvX92HsXQWU1qbcM6SWVi9vAdnL5mJztYaOmil+pLN5hf2CBqMmdc7VWG0ijUVLk/s7HUIxLrhuIRRa7DehydfzgCOiKjKGNARUVkppfDy4BFs2j6Ijf2D2LZrCADQ253AFafNw/nLe3DacdOgxWNV3lKqCUoB40fCBWOjh/RgzjOPTAqXJk473gjGnAIy28yZ1sZeZ0REVFMY0BFR5FKZLJ598wA29e/Bpu2DePvACADglGO7cN35i7F6eQ+WHjMFwgPj8ium8XMUlNKXHboGYn4zZ4cAlfF+jOYp+UFW91yg9STvQCxX2GMKEOPJAyIiahwM6IgoEsOjKfzs5b3Y1D+IJ3buxaFkCs1NMZy1cDo+9zvHY/WyHvR0Nthys1rn1Pj5oT/Tf/YK6tLj3oU9/GbOXMvZG5oS+UFWxyxgxiLvCotmsNbSCcT51UVERGTityIRhTYwlMRjxlLKZ17fj1RGYWqbhtXLenD+8h58aNEMtLfwY6ZqHrs1vwIhoP/+8HXArt+4L2NMjXjfb0wrzBfrnucRjFmXM3aykTQREVGEeKRFRIEppfDS7sPY2K8Hcf3vHgYAHDejHX941nE4f3kP3jdvKuJsLVB5qSSw72Vgzw5gTz+wZ7tzjzBAzzN78Uf5M2IzFtsCMZeiHq1deml6LpclIiKqCQzoiMjTWDqDZ14/gI397+Gx7Xvw7qFRiACnzpuKGz+6FKuX92DhzI5qb+bkkUkBB16fCNrM/w+8DqisfpuYBsxcohfwcJpt65oLXPtiZbebiIiIyoIBHREVGBoZxxM792Bj/yB+tnMvjo5nkNDi+NCiGfji+Ytx7tJZmN7BZXNllc0Ch97OD9r2bNdn4cwcNYnpFRpnLQNOukz/f9Zy/bK4VphDB7DxMxERUYNhQEdEAIC39h/NLaXc/NZBZLIKM6e04OMre3H+8ln4wMIZaNXi1d7MxqMUMPxeftC2px/YuzO/J1rXPD1gO2G1HrTNWqovk9QS7vfNxs9EREQNjwEd0SSVzSps3TWETdsHsal/EK/sOQIAWHrMFHz+dxZi9fIenNzbhRjz4aIzcqBwxm1Pv16QxNQ+Sw/c3vfpiRm3mUv0YiJhsPEzERFRQ2NARzSJJMcz+OWr+7CpfxCP7RjEviPjiMcEpx83DVecNg+rl/Vg3vS2am9m/Rsb1mfY7HluRwYnbtPapQdrJ11qBG1L9QCufUb1tpuIiIjqDgM6oga3d3gMj+8YxMb+Pfjlq3sxmspiSksTfmfJTJy/vAdnL56Frjat2ptZn1Kjek7b3h35wdvQ2xO3aUroyyNPWG3MuBmzblNms1IkERERlYwBHVGDUUrh1T1HsNHoD7dt1xCUAnq7E1jbNxerl/fg9OOmo7kpVu1NrR+ZtEtlydfyK0vOWAwcexrwvs9M5Ll1LwBi3NdERERUHgzoiBpAOpPF5rcOYlP/IDZuH8Rb+/VS9St6u7DuvMVYvXwWls/uhHBGyFs2q/duK6gsuXOisiRkorLkib87MeM2faFeWZKIiIioghjQEdWpI2Np/PzlvdjUP4jHd+7B0EgKzfEYzlw4HZ/90PFYvWwWZnd5VECczJTS89nsM257dtgqS841Kkuea8y4LfOvLElERERUQQzoiOrIu4eS2LRd7w/3zGv7MZ7JortNw7lLZuH85T340OKZ6Gjh2zrPyAFbjpsRwCUPTtymfaZRWfJTtsqSXdXbbiIiIqIAeORHVMOUUuh/9zA29g9i0/ZBvDhwGAAwf3obPn3mfJy/vAenzp+KpjhztDB2xKWy5HsTt2np0gO25WsmZtxYWZKIiIjqGAM6oirbsHUAdzyyE7uHkpjTncC1qxehp6tVD+L6B7H70ChEgFVzu/EXH1mCC5b3YOHMjsmbD5ce0ytL7rFXlnxr4jZNCX2GbeG5EzNus5YBnXNYWZKIiIgaiiilqr0NBfr6+tTmzZurvRlEZbdh6wBufOAFJFOZgutatRg+eMJMXLC8B+csnYWZU1qqsIVVlEkDB98onHHb/xqgjP0Va9Jz2qztAGYtA7rnA7F4dbefiIiIqAQiskUp1ed3O87QEVXRHY/sdAzmprU346m/PBeJ5kkQlCjlUFmyH9j7MpAZM25kqSy5/BMTwdu0hUBTc1U3n4iIiKiaGNARVdHuoaTj5QePjjdeMKcUcGRP4Yzb3h3A+JGJ23Ueqwdsx5+TX1myua16205ERERUoxjQEVVJNqvQosUwmsoWXDenu87L4icP2nLczMqSByZuY1aWXHmlZbnkUlaWJCIiIioCAzqiKvk/j7+K0VQWWlyQykzksia0OK6/cEkVt6wI40eNlgDb82fdht+duE1Lp7FU8uMTM24zlwEdM6u33URE/3979x2fZXX3cfxzCIGwQbYEBBUFRGVE1KqtVeuou4oIKLZVcLZaW62trW19fFqr1rprsfWpqIw4cCt14Kg7bEGWyAgoIHsFMs7zxx1LgCBKxpXxeb9evHLf55zc1zf1oq/8ONc5R5JqCAs6KQHjpn/OX1+ZzQ/6dOCofVtx279n/3eXy2tO2J8zendIOuK2CjbDirk7rnNbtQAoLkbrZkDrbrD30dvtLNnBnSUlSZIqiAWdVMlmfb6Oq8dM5uCOzfnjmQeSkZ7GmX0yk46VUlQIK0vbWXLutjtLtuwKe/aBXudt3WGyRWd3lpQkSapkFnRSJVq1YQsXjfiQRvXrMvz8vmSkJ1QAxQhrcneccftiNhTkFQ8KsEeX1Exb91O3zrq13NedJSVJkqoICzqpkhQUFnH5yIksXbOZMRcfRtumGRV/0Rhhw/IdZ9yWzYQt67aOa9qheGfJ75TYWXJ/d5aUJEmq4izopEpy0/Mf884nK7it/8H07tSi/C+waXXxBiXb7Sy5ccXWMQ1bpgq2XoNSO0q26ZFa99agefnnkSRJUoWzoJMqQfaHi/jXO/O58MgunN23jOvltmyA5bO2m3H7GNYt2TqmXpPULFu3U7bOuLXp4c6SkiRJNYwFnVTBJixYyfVPTeOorq341UnddhwwNRtevTG1pq1ZJhx7Axx0DhRsKd5ZcrsZt1Xz2XZnyf1Tj0q27ra1eGuW6c6SkiRJtUCIMe56VCXLysqKOTk5SceQyuyzNZs49e63aVQ/jacvP4LmDbfbTGRqNjz7U8jftLUtpEHjNqm1b0UFW9tadd32OIA2PdxZUpIkqYYKIUyIMWbtapwzdFIFycsvZNiICeTlFzJq6KE7FnOQmpkrWcxB6niATavhiKu2HgnQcl+oW79ygkuSJKnasKCTKkCMkeuemMpHS9bwwPlZdG3bpPSBa3JLby/Ig2N/W3EBJUmSVCPUSTqAVBMNf3MeT01ews+/tx/H9Whb+qAtG1KHdJemWRU5aFySJElVmgWdVM7Gz1rGzS/N5OSD2nP5d/ctfVBRETw5DIryIW27RzHTG6Q2RpEkSZJ2wYJOKkefLF/PT0dNonu7ptx69kGEne00+ervYeZzcOLNcPq90KwjEFJfT70rtculJEmStAuuoZPKydq8fIaOyKFeWh2GD+lLw3o7+es14SF4+0445CI49JLU8QIWcJIkSdoNFnRSOSgsivx01CQWrtjIyKGHkdmiYekD570Oz18N+x4HJ/7Zs+IkSZJUJhZ0Ujm4ddwsXp+1nP89syf9uuxR+qDls2DMEGi1H5z9f5DmXz9JkiSVTZnW0IUQTgwhzAohzA0hXPcV484KIcQQwi4PxpOqm6cnL+b+Nz5h8KGdGHzoXqUP2vAFjDwH6taDQWMgo2nlhpQkSVKNtNsFXQghDbgXOAnoAQwMIfQoZVwT4Erg/d29llRVTc1dzbWPT6Vflz343akHlD4oPw9GD4Z1n8PA0dC8U+WGlCRJUo1Vlhm6fsDcGOO8GOMWYDRweinj/gf4M5BXhmtJVc6ydXlc/PAEWjWuz98G96Fe3VL+OsUIz1wBi96DM++HTCepJUmSVH7KUtB1ABaVeJ9b3PZfIYQ+QMcY4/O7+rAQwrAQQk4IIWf58uVliCVVvM0FhVz6yERWb8xn+JC+tGxcv/SBb/wZpj2WOlfugDMrN6QkSZJqvAo7hy6EUAe4Hfj51xkfYxweY8yKMWa1bt26omJJZRZj5IanpjNhwSpu638wB+zZrPSBU7Ph9T9Br8Fw5NWVG1KSJEm1QlkKusVAxxLvM4vbvtQE6Am8HkKYDxwGPOPGKKruRry7gDE5i/jJMfty8kHtSx+08D14+nLY60g45Q6PJ5AkSVKFKEtB9yHQNYTQJYRQDzgXeObLzhjjmhhjqxhj5xhjZ+A94LQYY06ZEksJemfuF9z43AyO696Wnx23X+mDVs6D0YOgWUcY8HBqZ0tJkiSpAux2QRdjLACuAMYBHwPZMcbpIYQbQwinlVdAqapYuGIjl42cyN6tGvHXAQdTp04ps26bVsHIARCLYPBj0HAnZ9JJkiRJ5aBMJxvHGF8AXtiu7YadjD26LNeSkrRhcwFDR+QQI/zjgiyaZKTvOKgwH7KHwMpPYchT0HKfyg8qSZKkWqVMBZ1UGxQVRa7OnsycZet46Mf92Ktlox0HxQjPXw2fvgln/A06H1n5QSVJklTrVNgul1JNceercxg3fSnXn9yDo7ruZAfWd+6GiSPgqF9Ar0GVG1CSJEm1lgWd9BVe+ugz7nx1Dmf3zeTHR3QufdDHz8LLxefMfff6Ss0nSZKk2s2CTtqJmZ+v5ersKfTq2JybzuhJKO3ogSWT4Imh0KFv6lHLOv6VkiRJUuXxt0+pFCs3bOGih3JoklGX4ef3JSM9bcdBa3Jh5LnQqDUMHAXpDSo/qCRJkmo1N0WRtpNfWMTlj05k2brNZF98OG2aZuw4aPO6VDGXvzG1o2XjNpUfVJIkSbWeBZ20nZuem8G781Zw+zkH06tj8x0HFBXC4xfCshkwOBvadK/8kJIkSRIWdNI2Rn+wkIfeXcDQo7rwgz6ZpQ8adz3MGQcn/wX2Pa5yA0qSJEkluIZOKpYzfyW/ffojjuraiutO2sms2wcPwPt/g8Muh0MuqtyAkiRJ0nYs6CRgyepNXPLIBDJbNOSegX1Iq1PKjpZzXoEXr4X9ToLj/6fyQ0qSJEnb8ZFL1XqbthQy7OEc8vKLGD2sL80apu84aOl0eOyH0PYAOOsfUKeUXS8lSZKkSmZBp1otxsgvn5jK9CVr+ceQLPZt02THQeuWwsgBUL8xDByT+ipJkiRVARZ0qtXuf2Mez0xZwjUn7M+x3dvuOGDLRhg9EDaugB+9CM06VH5ISZIkaScs6FRrvTZzKbeMm8mpB+/JZUfvs+OAoiJ46hJYPBEGPAJ79qr8kJIkSdJXsKBTrTR32XquHDWZHu2bcstZBxFCKZugvPY/MONpOP4m6H5K5YeUJEmSdsFdLlXrrNmUz7AROdRPr8PwIVk0qFfKBieTHoH/3A59fwiHX1HpGSVJkqSvwxk61SqFRZGfjprEolUbGTn0MDo0b7DjoE/fgmevhL2Phu/fBqXN3kmSJElVgAWdapVbXprJG7OX86cfHMghnffYccAXc2HMebDHPtD/IUgr5QgDSZIkqYrwkUvVGmMn5fL3N+dx/mF7MbBfpx0HbFwJI/tDnbowOBsaNK/8kJIkSdI34AydaoUpi1bzyyemcdjee3DDqT12HFCwGUYPhjWL4YJnoUXnSs8oSZIkfVMWdKrxlq3NY9jDObRuXJ/7BvclPW27iekYU2vmFr4DZ/0TOh2aTFBJkiTpG7KgU422uaCQSx6ZwNpNBTxx6bfYo1G9HQe9dRtMGQXfvR4OPLvyQ0qSJEm7yYJONVaMkd+M/YiJC1fzt8F96LFn0x0HffQEvHYTHDQAvn1N5YeUJEmSysBNUVRj/eud+Tw2IZefHtuVkw5sv+OARR/A2Euh0+Fw2t0eTyBJkqRqx4JONdLbc7/gpuc/5vgebbnq2K47Dlg1H0YNhKZ7woBHoW79Ss8oSZIklZUFnWqcBSs2cNmjE9mndSNuH9CLOnW2m3nLWwMjB0BRPgx+DBq1TCaoJEmSVEauoVONsn5zAUNH5BAC/GPIITSuv90tXpgP2RfAirlw/lhoVcrsnSRJklRNWNCpxigqivxszGQ+Wb6BET/uR6eWDbcdECO8cA3MGw+n3wtdvp1MUEmSJKmc+Milaow7XpnNyzOW8puTu3PEvq12HPDefTDh/+CIq6D3eZUfUJIkSSpnFnSqEV6Y9hl3vTaXc7Iy+eG3Ou84YOYLMO566H4qHPu7Ss8nSZIkVQQLOlV7M5as5efZU+jTqTn/c0ZPwvbHDyyZDE9cCHv2gjOHQx1ve0mSJNUM/maram3F+s0MHZFDswbp3H9+X+rXTdt2wNolMOpcaLAHDBwN9RqW/kGSJElSNeSmKKq28guLuOzRiXyxfjOPXXI4bZpkbDtg8/rU8QSb18GPx0GTdskElSRJkiqIBZ2qrRufncH7n67kjgG9OCiz+badRYXw5FBY+hEMHAPteiYTUpIkSapAFnSqlka+v5CH31vAxd/emzN6d9hxwMs3wKwX4KRbYb/jKz+gJEmSVAlcQ6dq54NPV3LD0x9x9P6tufbEbjsOyHkQ3r0H+l0Mhw6r/ICSJElSJbGgU7WyePUmLn1kAp32aMid5/Ymrc52O1p+8ho8/wvoejyc8MdkQkqSJEmVxIJO1camLYUMG5HDloIiHrggi2YN0rcdsGwmZF8ArbvB2Q9Cmk8US5IkqWbzN15VCzFGrnl8CjM+W8uDFxzCPq0bbztg/XIY2R/SG8CgMVC/STJBJUmSpEpkQadq4b7XP+G5qZ9x3Und+G63Ntt25m+C0QNTRd2PnofmHZMJKUmSJFUyCzpVea/MWMpt/57F6b325OJv771tZ1ERPHUZ5H4I5zwMHfomE1KSJElKgGvoVKXNXbaOq8ZMpueezfjzWQcRwnaboLz+J5j+JBz3B+hxWjIhJUmSpIRY0KnKWrMxn4seyiEjPY3hQ/qSkZ627YDJo+DNW6D3+XDElcmElCRJkhJkQacqqaCwiCtGTWTx6k3cf14f2jdrsO2A+W/DMz+BzkfBybfD9jN3kiRJUi3gGjpVSX9+aSZvzfmCP591IFmd99i2c8UnMGYwtOgMAx6GuvUSyShJkiQlzRk6VTlPTMjlgbc+5Yff6syAQzpt27lxJYw8Bwip4wkatEgkoyRJklQVOEOnKmXyotX8auw0Dt+7Jdef3H3bzoItkD0EVi+EIU9Dy32SCSlJkiRVERZ0qjKWrs1j2Igc2jatz32D+5CeVmICOUZ47mcw/y04czjs9a3kgkqSJElVRJkeuQwhnBhCmBVCmBtCuK6U/ktCCNNCCJNDCP8JIfQoy/VUc+XlF3LxwxNYv7mAB4Zk0aLRduvi/vNXmPwIfOeXcPCAZEJKkiRJVcxuF3QhhDTgXuAkoAcwsJSCbWSM8cAYYy/gFuD23U6qGivGyPVjP2LyotXcfk4vurVruu2A6U/Bq3+AnmfD0b9KJqQkSZJUBZVlhq4fMDfGOC/GuAUYDZxeckCMcW2Jt42AWIbrqYZ68O35PDExl6uO68qJPdtt25k7AcZeDJn94PR7PZ5AkiRJKqEsa+g6AItKvM8FDt1+UAjhcuBqoB5wzM4+LIQwDBgG0KlTp50NUw3z1pzl/O/zMzjxgHb89Jiu23auXgSjzoXGbWHgKEjPSCakJEmSVEVV+LEFMcZ7Y4z7AL8EfvMV44bHGLNijFmtW7eu6FiqAuZ/sYErRk5iv7ZN+Ms5B1OnTonZt7y1MHIAFGyGQdnQqFVyQSVJkqQqqiwF3WKgY4n3mcVtOzMaOKMM11MNsi4vn4tG5FAnwANDsmhUv8RkcWEBPP5jWD4TznkI2nRLLqgkSZJUhZWloPsQ6BpC6BJCqAecCzxTckAIoeQzdCcDc8pwPdUQRUWRn42ZzKdfbODewX3ouEfDbQeM+xXMfRlOuR32+W4yISVJkqRqYLfX0MUYC0IIVwDjgDTgwRjj9BDCjUBOjPEZ4IoQwnFAPrAKuKA8Qqt6u/3l2bzy8TL+cNoBfGuf7R6lfP/v8MFw+NZPoO8PE8knSZIkVRdlOlg8xvgC8MJ2bTeUeH1lWT5fNc9zU5dwz/i5nHtIR4Ycvte2nbPHwUvXQbdT4Lg/JBNQkiRJqkYqfFMU6UvTl6zhmsemkrVXC248vSeh5BEEn09LrZtrdyD8YDjUSUsuqCRJklRNWNCpUnyxfjPDRkygecN0/nZeX+rVLXHrrfs8taNl/aYwcAzUa5RcUEmSJKkaKdMjl9LXsaWgiMsemcgX6zfz+CXfonWT+iU6N6SKuU2r4ccvQdP2yQWVJEmSqhkLOlW4Pzw7nQ/mr+TOc3txYGazrR1FRfDkMPhsSurg8PYHJRdSkiRJqoYs6FShHnlvAY++v5BLvrMPp/fqsG3nq7+Hmc/BCX+C/U9KJJ8kSZJUnbmGThXm/Xkr+P0z0zmmWxuuOWH/bTsnPARv3wlZF8JhlyYTUJIkSarmLOhUIXJXbeTSRyfSqWVD7ji3F2l1SuxoOe91eP5q2OcYOOkWKLnbpSRJkqSvzYJO5W7jlgKGjphAfmER/xiSRdOM9K2dy2fBmCHQsiv0/xek+dSvJEmStLss6FSuYoxc89hUZn2+lrsH9mbv1o23dm74AkaeA3XrwaAxkNFs5x8kSZIkaZecHlG5unf8XJ6f9hm//n43jt6/zdaO/DwYPTh15twFz0GLvZILKUmSJNUQFnQqNy/PWMpt/57Nmb07MPSovbd2xAjPXAGL3oOz/w86HpJcSEmSJKkG8ZFLlYvZS9dx1ehJHJTZjD/94EBCyY1O3rgFpj0Gx/wWev4guZCSJElSDWNBpzJbvXELQ0fk0LB+XYafn0VGetrWzqmPwet/hIMHwVE/Ty6kJEmSVANZ0KlMCgqLuGLkJD5bncf95/WlXbOMrZ0L34OnL4O9joRT7/R4AkmSJKmcuYZOZfLHF2byn7lfcMvZB9F3rxZbO1Z+CqMHQbOOMODh1M6WkiRJksqVM3TabY/lLOLBtz/lR0d05pysjls7Nq1OHU8Qi2DwY9Bwj+RCSpIkSTWYM3TaLRMXruL6sR9xxL4tuf773bd2FOZD9pDUDN2Qp6DlPsmFlCRJkmo4Czp9Y5+vyePihyfQrlkG9wzsQ9204oneGOH5q+HTN+CMv0HnI5MNKkmSJNVwFnT6RvLyC7n44Rw2bi7g0YsOpUWjEmvj3rkbJo5I7WbZa1ByISVJkqRawoJOX1uMkV8/OY0puWsYfn5f9mvbZGvnx8/CyzdAjzPgu79JLqQkSZJUi7gpir62f7z1KU9OWszV39uP4w9ot7VjySR4Yih06Atn3g91vK0kSZKkyuBv3vpa3pi9nD+9+DHfP7AdPzlm360da3Jh5LnQqDUMHAXpDZILKUmSJNUyFnTapU+/2MBPRk5kv7ZNuPXsgwlfHhC+eV2qmNuyAQaNgcZtkg0qSZIk1TKuodNXWpuXz0UPfUjdtDo8MCSLRvWLb5miQnj8Qlg2AwZnQ9seyQaVJEmSaiELOu1UYVHkqtGTWbBiI49cdCgd92i4tXPc9TBnHJz8F9j3uORCSpIkSbWYj1xqp/7y71m8NnMZvzu1B4ft3XJrxwcPwPt/g8Mug0MuSi6gJEmSVMtZ0KlUz0xZwn2vf8LAfp0477C9tnbMeQVevBb2OxGOvym5gJIkSZIs6LSjjxav4drHp3BI5xb84bQDtm6CsnQGPPZDaHsAnPVPqJOWaE5JkiSptrOg0zaWr9vMsBE57NGwHn87ry/16hbfIuuWwshzoF4jGDgG6jdONqgkSZIkN0XRVlsKirj0TtS9jwAAGCFJREFUkQms3LiFxy/5Fq0a10915G+C0QNh4wr40QvQrEOyQSVJkiQBFnQqFmPkd898RM6CVdw9sDc9OzRLdRQVwdhLYPFEGPAI7Nk72aCSJEmS/suCTgA88t4CRn2wiMuO3odTD95za8f4m2DGU6kNULqfklxASZIkSTtwDZ1495MV/OHZGRzbrQ2/OH7/rR2THoW3/gJ9fwiHX5FYPkmSJEmls6Cr5Rat3Mhlj06gc6tG3HFuL+rUKd7R8tO34NkrYe+j4fu3wZc7XUqSJEmqMizoarENmwsYOiKHwqLIA0OyaJKRnur4Yi6MOQ/22Bv6PwRp6ckGlSRJklQq19DVUkVFkV88NoXZS9fxrx/1o0urRqmOjSthZP/UGXODxkCD5skGlSRJkrRTFnS11D3j5/LiR5/zm5O78+39WqcaCzbD6MGwZjFc8Czs0SXZkJIkSZK+kgVdLTRu+ufc/vJsftC7AxceWVy0xZhaM7fwHTjrn9Dp0GRDSpIkSdol19DVMrM+X8fVYyZzcMfm/PEHBxK+3Ozkrdtgyig4+tdw4NnJhpQkSZL0tVjQ1SKrNmzhohEf0qh+XYaf35eM9LRUx0dPwGs3wUED4DvXJhtSkiRJ0tfmI5e1REFhEVeMmsjSNZsZffFhtG2akepY9AGMvRQ6HQ6n3e3xBJIkSVI1YkFXS9z0/Me8PXcFt/U/mD6dWqQaV82HUQOhaXsY8CjUrZ9oRkmSJEnfjI9c1gLZHy7iX+/M58Iju3B238xUY94aGDkAivJh0GPQqGWyISVJkiR9Y87Q1XATFqziN099xFFdW/Grk7qlGgvzIfsCWDEXznsSWu+XbEhJkiRJu8WCrgb7bM0mLn54Au2bZ3D3wN7UTauTOp7gxWth3vjUmrm9v5N0TEmSJEm7yYKuhsrLL+TihyewaUsBo4YeSvOG9VId7/0Nch6EI66CPkOSDSlJkiSpTCzoaqAYI9c9MZVpi9cw/PwsurZtkuqY9SKM+zV0PxWO/V2yISVJkiSVmZui1EDD35zHU5OX8PPv7cf3erRNNX42BR6/EPbsBWcOhzr+p5ckSZKquzL9Vh9CODGEMCuEMDeEcF0p/VeHEGaEEKaGEF4NIexVlutp116ftYybX5rJyQe25/Lv7ptqXLsERp4LDVrAwNFQr2GyISVJkiSVi90u6EIIacC9wElAD2BgCKHHdsMmAVkxxoOAx4Fbdvd62rVPlq/nJ6Mm0b1dU27tfxAhBNi8PnU8wea1MGgMNGmXdExJkiRJ5aQsM3T9gLkxxnkxxi3AaOD0kgNijONjjBuL374HZJbhevoKa/PyGToih3ppdRg+pC8N69WFokJ4cigs/QjOfhDa9Uw6piRJkqRyVJaCrgOwqMT73OK2nbkQeHFnnSGEYSGEnBBCzvLly8sQq/YpLIpcOWoSC1ds5L7BfchsUfxI5cs3wKwX4MSbYb8Tkg0pSZIkqdxVys4YIYTzgCzg1p2NiTEOjzFmxRizWrduXRmxaoxbx81i/Kzl/P60Azh075apxpwH4d17oN8wOPTiZANKkiRJqhBlObZgMdCxxPvM4rZthBCOA64HvhNj3FyG66kUT09ezP1vfMLgQztx3mHFe8588ho8/wvY93twwp+SDShJkiSpwpRlhu5DoGsIoUsIoR5wLvBMyQEhhN7A34HTYozLynAtlWJa7hqufXwq/brswe9OPSDVuGwmZF8Arbul1s2ledSgJEmSVFPtdkEXYywArgDGAR8D2THG6SGEG0MIpxUPuxVoDDwWQpgcQnhmJx+nb2jZujyGPZxDq8b1uW9wH+rVrQPrl8PI/lA3I7WjZUbTpGNKkiRJqkBlmr6JMb4AvLBd2w0lXh9Xls9X6TYXFHLpIxNZvTGfxy89nFaN60P+Jhg9MFXU/eh5aN5x1x8kSZIkqVrzebxqJsbIDU9NZ8KCVdw7qA8H7NkMiorgqcsg90M4ZwR06Jt0TEmSJEmVoFJ2uVT5GfHuAsbkLOKK7+7LyQe1TzW+/ieY/iQc93vocfpXfbskSZKkGsSCrhp5Z+4X3PjcDI7r3parv7dfqnHyKHjzFuh9HhxxVbIBJUmSJFUqC7pqYtHKjVw2ciJ7t2rEXwccTJ06Aea/Dc/8BDofBSf/FUJIOqYkSZKkSmRBVw1s2FzA0BE5xAgPDMmiSUY6rPgExgyGFp1hwMNQt17SMSVJkiRVMgu6Kq6oKHJ19mRmL13HPYN607lVI9i0CkaeA4TU8QQNWiQdU5IkSVIC3OWyirvrtTmMm76U357Sg6O6toaCLTDmfFi9EIY8DS33STqiJEmSpIRY0FVhL330GXe8Moez+mTy4yM6Q4zw/M9g/ltw5nDY61tJR5QkSZKUIB+5rKJmfr6Wq7On0Ktjc/73zJ6EEODtO2DSI/Dta+HgAUlHlCRJkpQwC7oqaOWGLVz0UA5NMuoy/Py+ZKSnwfSn4JXfQ8+z4Lu/TjqiJEmSpCrARy6rmPzCIi5/dCLL1m0m++LDadM0A3InwNiLIbMfnH6fxxNIkiRJApyhq3Juem4G785bwc0/OJBeHZvD6kUw6lxo3AbOHQnpGUlHlCRJklRFOENXhYz+YCEPvbuAoUd14Qd9MiFvLYwcAAV5cMGz0Lh10hElSZIkVSEWdFVEzvyV/Pbpjziqayt+eWI3KCyAx38My2fCeY9Dm25JR5QkSZJUxVjQVQFLVm/ikkcmkNmiIfcM7EPdtDrwwi9h7stwyh2wzzFJR5QkSZJUBVnQJWzTlkKGPZxDXn4Ro4f1pVnDdHj/7/DBcDj8Csj6UdIRJUmSJFVRFnQJijHyyyemMn3JWv4xJIt92zSB2ePgpetg/5PhezcmHVGSJElSFeYulwm6/415PDNlCb84fn+O7d4WPp+WWjfXtiec9QDUSUs6oiRJkqQqzIIuIeNnLuOWcTM55aD2XHb0PrDu89SOlvWbwqAxUK9R0hElSZIkVXE+cpmAucvW89NRk+jRvim3nn0wIX9jqpjbtBp+/CI03TPpiJIkSZKqAQu6SrZmUz7DRuRQP70Ow4dk0aBugOxh8NkUGDgK2h+cdERJkiRJ1YQFXSUqLIr8dNQkFq3ayMihh9GheQN4+QaY+Ryc8CfY/6SkI0qSJEmqRizoKtEtL83kjdnL+eOZB3JI5z1g4gh4+07IuhAOuzTpeJIkSZKqGTdFqSRjJ+Xy9zfncf5hezHo0E4w7w147mepQ8NPugVCSDqiJEmSpGrGGbpKMDV3Nb98YhqHdtmDG07tActnQ/b50HJf6P8vSPM/gyRJkvSl/Px8cnNzycvLSzpKhcvIyCAzM5P09PTd+n4riQq2bG0ew0ZMoHXj+tw3uA/peatgZH9IqweDsiGjWdIRJUmSpColNzeXJk2a0LlzZ0INfpItxsiKFSvIzc2lS5cuu/UZPnJZgTYXFHLJIxNYsymfB4Zk0TIDGD0I1n4G546CFnslHVGSJEmqcvLy8mjZsmWNLuYAQgi0bNmyTDORztBVkBgjvxn7ERMXruZvg/vQo30TeHIYLHoPzn4QOh6SdERJkiSpyqrpxdyXyvpzOkNXQf71znwem5DLT4/Zl5MObA9v3ALTsuGY30DPs5KOJ0mSJKkGsKCrAG/P/YKbnv+Y43u05arj9oOpj8Hrf4SDB8JRv0g6niRJklSjPDVpMUfc/BpdrnueI25+jacmLS7T561evZr77rvvG3/f97//fVavXl2ma39TFnTlbMGKDVz26ET2ad2I2wf0ok7u+/D0ZbDXEXDqnR5PIEmSJJWjpyYt5ldPTmPx6k1EYPHqTfzqyWllKup2VtAVFBR85fe98MILNG/efLevuztcQ1eO1m8uYOiIHEKAB4Zk0XjDotQmKM0yYcAjULd+0hElSZKkauUPz05nxpK1O+2ftHA1WwqLtmnblF/ItY9PZdQHC0v9nh57NuV3px6w08+87rrr+OSTT+jVqxfp6elkZGTQokULZs6cyezZsznjjDNYtGgReXl5XHnllQwbNgyAzp07k5OTw/r16znppJM48sgjeeedd+jQoQNPP/00DRo02I3/Bb6aM3TlpKgo8rMxk/lk+QbuHdSHvRrmw8hzoKgQBj0GDfdIOqIkSZJU42xfzO2q/eu4+eab2WeffZg8eTK33norEydO5M4772T27NkAPPjgg0yYMIGcnBzuuusuVqxYscNnzJkzh8svv5zp06fTvHlznnjiid3O81WcoSsnd7w6h5dnLOV3p/bgiC7N4JGzYOWnMOQpaLVv0vEkSZKkaumrZtIAjrj5NRav3rRDe4fmDRhz8eHlkqFfv37bnBN31113MXbsWAAWLVrEnDlzaNmy5Tbf06VLF3r16gVA3759mT9/frlk2Z4zdOXgxWmfcderc+jfN5MfHr4XPH81fPpGas1c5yOTjidJkiTVWNecsD8N0tO2aWuQnsY1J+xfbtdo1KjRf1+//vrrvPLKK7z77rtMmTKF3r17l3qOXP36W5dbpaWl7XL93e5yhq6MZixZy9XZU+jTqTk3ndmT8O49MHEEHPVz6D046XiSJElSjXZG7w4A3DpuFktWb2LP5g245oT9/9u+O5o0acK6detK7VuzZg0tWrSgYcOGzJw5k/fee2+3r1MeLOjKYOWGLQwdkUOzBuncf15f6s95AV6+AXqcAd/9TdLxJEmSpFrhjN4dylTAba9ly5YcccQR9OzZkwYNGtC2bdv/9p144oncf//9dO/enf3335/DDjus3K67O0KMMdEApcnKyoo5OTlJx/hK+YVFnP/P95m4cDWPXXw4B6d9Cg+eBG0PgB8+B+nlv4ONJEmSVBt8/PHHdO/ePekYlaa0nzeEMCHGmLWr73UN3W668dkZvDdvJbecdRAHN90AI8+FRq1h4CiLOUmSJEmVwkcud8PI9xfy8HsLuPjbe3NGj2bw4ImwZQNcOBYat0k6niRJkqRawoLuG/pw/kp+98xHfGe/1lx7fFfIHgzLpqfOmmvbI+l4kiRJkmoRC7qv4alJi/+7a04I0LJRPe4a2Ju0V34Ls1+C798GXY9LOqYkSZKkWsY1dLvw1KTF/OrJaSxevYkIFEVYm1fA/BfvhPfug0MvhX5Dk44pSZIkqRZyhm4Xbh03i035hdu0HVY0iQOm3gpdT4AT/jehZJIkSZJqO2fodmHJ6k3bvN8vLOKe9LuYXdQRzv4n1EnbyXdKkiRJqhRTs+GvPeH3zVNfp2ZX6uUbN25cqdcryRm6XdizeQP6rn2Za+tms2f4giLqsIH6XN/gN4yt3yTpeJIkSVLtNjUbnv0p5BdPxKxZlHoPcNA5yeWqJBZ0u3BHjzn0nPAPGoQtANShiPqxgF8fsCrhZJIkSVIt8OJ18Pm0nffnfgiFm7dty98ET18BEx4q/XvaHQgn3bzTj7zuuuvo2LEjl19+OQC///3vqVu3LuPHj2fVqlXk5+dz0003cfrpp3/Tn6bclemRyxDCiSGEWSGEuSGE60rp/3YIYWIIoSCEcHZZrpWUQz65+7/F3JcyQj6HfHJ3QokkSZIk/df2xdyu2r+GAQMGkJ299bHN7OxsLrjgAsaOHcvEiRMZP348P//5z4kx7vY1ystuz9CFENKAe4HvAbnAhyGEZ2KMM0oMWwj8EPhFWUImak3uN2uXJEmSVH6+YiYNSK2ZW7Nox/ZmHeFHz+/WJXv37s2yZctYsmQJy5cvp0WLFrRr146f/exnvPnmm9SpU4fFixezdOlS2rVrt1vXKC9leeSyHzA3xjgPIIQwGjgd+G9BF2OcX9xXVIbrJKtZ5k5ukMzKzyJJkiRpW8fesO0aOoD0Bqn2Mujfvz+PP/44n3/+OQMGDODRRx9l+fLlTJgwgfT0dDp37kxeXl4Zw5ddWR657ACUrHRyi9tqlmNvSN0QJZXDDSJJkiSpHBx0Dpx6V2pGjpD6eupdZd4QZcCAAYwePZrHH3+c/v37s2bNGtq0aUN6ejrjx49nwYIF5ZO/jKrMpighhGHAMIBOnTolnKaEL2+EV29MPWbZLDNVzNWCHXMkSZKkauGgc8r99/MDDjiAdevW0aFDB9q3b8/gwYM59dRTOfDAA8nKyqJbt27ler3dVZaCbjHQscT7zOK23RJjHA4MB8jKykp+dWFJFXCDSJIkSarapk3burtmq1atePfdd0sdt379+sqKtIOyPHL5IdA1hNAlhFAPOBd4pnxiSZIkSZJ2ZbcLuhhjAXAFMA74GMiOMU4PIdwYQjgNIIRwSAghF+gP/D2EML08QkuSJEmSyriGLsb4AvDCdm03lHj9IalHMSVJkiTpa4sxEkJIOkaFK+tZdmU6WFySJEmSyltGRgYrVqyoEgd3V6QYIytWrCAjI2O3P6PK7HIpSZIkSQCZmZnk5uayfPnypKNUuIyMDDIzd/+hRgs6SZIkSVVKeno6Xbp0STpGteAjl5IkSZJUTVnQSZIkSVI1ZUEnSZIkSdVUqIo7x4QQlgMLks5RilbAF0mHUI3l/aWK5P2liuT9pYrk/aWKVlXvsb1ijK13NahKFnRVVQghJ8aYlXQO1UzeX6pI3l+qSN5fqkjeX6po1f0e85FLSZIkSaqmLOgkSZIkqZqyoPtmhicdQDWa95cqkveXKpL3lyqS95cqWrW+x1xDJ0mSJEnVlDN0kiRJklRNWdBJkiRJUjVlQfc1hBBODCHMCiHMDSFcl3Qe1SwhhAdDCMtCCB8lnUU1TwihYwhhfAhhRghhegjhyqQzqeYIIWSEED4IIUwpvr/+kHQm1TwhhLQQwqQQwnNJZ1HNEkKYH0KYFkKYHELISTrP7nIN3S6EENKA2cD3gFzgQ2BgjHFGosFUY4QQvg2sB0bEGHsmnUc1SwihPdA+xjgxhNAEmACc4f+HqTyEEALQKMa4PoSQDvwHuDLG+F7C0VSDhBCuBrKApjHGU5LOo5ojhDAfyIoxVsVDxb82Z+h2rR8wN8Y4L8a4BRgNnJ5wJtUgMcY3gZVJ51DNFGP8LMY4sfj1OuBjoEOyqVRTxJT1xW/Ti//4L8UqNyGETOBk4B9JZ5GqKgu6XesALCrxPhd/GZJUDYUQOgO9gfeTTaKapPhxuMnAMuDlGKP3l8rTHcC1QFHSQVQjReDfIYQJIYRhSYfZXRZ0klQLhBAaA08AV8UY1yadRzVHjLEwxtgLyAT6hRB8dFzlIoRwCrAsxjgh6SyqsY6MMfYBTgIuL14GU+1Y0O3aYqBjifeZxW2SVC0Ur216Ang0xvhk0nlUM8UYVwPjgROTzqIa4wjgtOJ1TqOBY0IIjyQbSTVJjHFx8ddlwFhSS62qHQu6XfsQ6BpC6BJCqAecCzyTcCZJ+lqKN634J/BxjPH2pPOoZgkhtA4hNC9+3YDUBmIzk02lmiLG+KsYY2aMsTOp379eizGel3As1RAhhEbFm4URQmgEHA9Uyx3HLeh2IcZYAFwBjCO1mUB2jHF6sqlUk4QQRgHvAvuHEHJDCBcmnUk1yhHA+aT+ZXty8Z/vJx1KNUZ7YHwIYSqpfwB9Ocbo1vKSqoO2wH9CCFOAD4DnY4wvJZxpt3hsgSRJkiRVU87QSZIkSVI1ZUEnSZIkSdWUBZ0kSZIkVVMWdJIkSZJUTVnQSZIkSVI1ZUEnSaqxQgiFJY5rmBxCuK4cP7tzCKFanlkkSao56iYdQJKkCrQpxtgr6RCSJFUUZ+gkSbVOCGF+COGWEMK0EMIHIYR9i9s7hxBeCyFMDSG8GkLoVNzeNoQwNoQwpfjPt4o/Ki2E8EAIYXoI4d8hhAaJ/VCSpFrJgk6SVJM12O6RywEl+tbEGA8E7gHuKG67G3goxngQ8ChwV3H7XcAbMcaDgT7A9OL2rsC9McYDgNXAWRX880iStI0QY0w6gyRJFSKEsD7G2LiU9vnAMTHGeSGEdODzGGPLEMIXQPsYY35x+2cxxlYhhOVAZoxxc4nP6Ay8HGPsWvz+l0B6jPGmiv/JJElKcYZOklRbxZ28/iY2l3hdiGvTJUmVzIJOklRbDSjx9d3i1+8A5xa/Hgy8Vfz6VeBSgBBCWgihWWWFlCTpq/gviZKkmqxBCGFyifcvxRi/PLqgRQhhKqlZtoHFbT8B/i+EcA2wHPhRcfuVwPAQwoWkZuIuBT6r8PSSJO2Ca+gkSbVO8Rq6rBjjF0lnkSSpLHzkUpIkSZKqKWfoJEmSJKmacoZOkiRJkqopCzpJkiRJqqYs6CRJkiSpmrKgkyRJkqRqyoJOkiRJkqqp/wepJ2rqBYludwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "solver = Solver(model, data,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                lr_decay=0.8,\n",
    "                num_epochs=5, batch_size=100,\n",
    "                print_every=100)\n",
    "solver.train()\n",
    "best_model = model  # 一次过确定 不比较了\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Test your model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.525\n",
      "Test set accuracy:  0.523\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
